From 7b39a5742170c066d22ab21a0c97211e47d418ca Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 21:28:11 +0200 Subject: [PATCH] - some proof reading --- .../notebooks/09_example_Sim_and_xRL.ipynb | 8992 +---------------- 1 file changed, 252 insertions(+), 8740 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index 3de54175..b0502123 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3ba8dd1e", + "id": "3efad297", "metadata": { "id": "e62e00c9" }, @@ -12,17 +12,17 @@ }, { "cell_type": "markdown", - "id": "3a2ed19f", + "id": "20db7561", "metadata": { "id": "fb3aa803" }, "source": [ - "Welcome to this tutorial on **Explainable Reinforcement Learning (XRL)**! In this guide, we'll explore how to interpret and explain the decisions made by reinforcement learning agents using the SHAP (SHapley Additive exPlanations) library. We'll work through a practical example involving an the simulation simulation in a reinforcement learning setting, and demonstrate how to compute and visualize feature attributions for the agent's actions." + "Welcome to this tutorial on **Explainable Reinforcement Learning (XRL)**! In this guide, we will explore how to interpret and explain the decisions made by reinforcement learning agents using the SHAP (SHapley Additive exPlanations) library. Through a practical example involving a simulation in a reinforcement learning setting, we'll demonstrate how to compute and visualize feature attributions for the agent's actions." ] }, { "cell_type": "markdown", - "id": "131db756", + "id": "046a32c0", "metadata": { "id": "0d793362" }, @@ -32,7 +32,7 @@ }, { "cell_type": "markdown", - "id": "667dc923", + "id": "f45c2689", "metadata": { "id": "87bdf688" }, @@ -47,7 +47,7 @@ "\n", " 2.2 Introduction to SHAP Values\n", "\n", - "3. [Calculating SHAP values](#3-calculating-shap-values)\n", + "3. [Calculating SHAP Values](#3-calculating-shap-values)\n", "\n", " 3.1. [Loading and Preparing Data](#loading-and-preparing-data)\n", "\n", @@ -60,7 +60,7 @@ }, { "cell_type": "markdown", - "id": "ed057a2a", + "id": "5adc5d28", "metadata": { "id": "5e8c7fec" }, @@ -70,76 +70,93 @@ }, { "cell_type": "markdown", - "id": "3a3562cf", + "id": "a5b2ef10", "metadata": { "id": "06e91420" }, "source": [ - "Reinforcement Learning (RL) has achieved remarkable success in various domains, such as game playing, robotics, and autonomous systems. However, RL models, particularly those using deep neural networks, are often seen as black boxes due to their complex architectures and non-linear computations. This opacity poses challenges in understanding and trusting the decisions made by RL agents, especially in critical applications." + "Reinforcement Learning (RL) has achieved remarkable success in various domains, such as game playing, robotics, and autonomous systems. However, RL models, particularly those using deep neural networks, are often seen as **black boxes** due to their complex architectures and non-linear computations. This opacity makes it challenging to understand and trust the decisions made by RL agents, especially in critical applications where transparency is essential." ] }, { "cell_type": "markdown", - "id": "4c610270", + "id": "d1fcbef6", "metadata": { "id": "47b1e7ab" }, "source": [ - "**Explainable Reinforcement Learning (XRL)** aims to bridge this gap by providing insights into the agent's decision-making process. By leveraging explainability techniques, we can interpret the actions of an RL agent, understand the influence of input features, and potentially improve the model's performance and fairness." + "**Explainable Reinforcement Learning (XRL)** aims to bridge this gap by providing insights into an agent's decision-making process. By leveraging explainability techniques, we can:\n", + "- Interpret the actions of an RL agent.\n", + "- Understand the influence of input features on decisions.\n", + "- Potentially improve the model's performance, fairness, and transparency." ] }, { "cell_type": "markdown", - "id": "9978b51b", + "id": "5ced3235", "metadata": { "id": "ec0717c1" }, "source": [ - "In this tutorial, we will demonstrate how to apply SHAP values to a trained actor neural network within an RL framework to explain the agent's actions." + "In this tutorial, we will demonstrate how to apply SHAP values to a trained actor neural network in an RL framework to explain the agent's actions." ] }, { "cell_type": "markdown", - "id": "23bed9c3", + "id": "49f01746", "metadata": { - "id": "0d59bb0a" + "id": "0d59bb0a", + "lines_to_next_cell": 0 }, "source": [ - "### 1.1 Run an the simulation MADRL Simulation \n", + "### 1.1 Running a MADRL Simulation \n", "\n", - "In ASSUME, we implement RL agents using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. Key aspects include:\n", + "In this tutorial, we will simulate RL agents using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. The agents operate in a market-splitting environment where they interact and learn optimal strategies over time. Here’s a breakdown of the key components:\n", "\n", + "- **Observations**: Each agent receives observations, including market forecasts, unit-specific information, and past actions.\n", + "- **Actions**: The agents decide on bidding strategies, such as bid prices for both inflexible and flexible capacities.\n", + "- **Rewards**: The agents are rewarded based on profits and opportunity costs, helping them learn optimal bidding strategies.\n", + "- **Algorithm**: We utilize a multi-agent version of the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm, which ensures stable learning even in non-stationary environments.\n", "\n", - "- **Observations**: Each agent receives observations comprising market forecasts, unit-specific information, and past actions.\n", - "- **Actions**: Agents decide on bidding strategies, such as bid prices for inflexible and flexible capacities.\n", - "- **Rewards**: Agents receive rewards based on profits and opportunity costs, guiding them to learn optimal bidding strategies.\n", - "- **Algorithm**: We utilize a multi-agent version of the TD3 algorithm, ensuring stable learning in a non-stationary environment.\n", + "For a more detailed explanation of the RL configurations, refer to the [Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial).\n", "\n", - "For a deep dive into the RL configurations we refer to one of the other tutorials, such as\n", - "[Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial)\n", + "### Key Aspects of the Simulation\n", "\n", - "Agents need observations to make informed decisions. Observations include:\n", + "Agents require **observations** to make informed decisions, which include:\n", "\n", - "- **Residual Load Forecast**: Forecasted net demand over the next 24 hours.\n", + "- **Residual Load Forecast**: Forecasted net demand (electricity demand minus renewable generation) over the next 24 hours.\n", "- **Price Forecast**: Forecasted market prices over the next 24 hours.\n", - "- **Marginal Cost**: Current marginal cost of the unit.\n", - "- **Previous Output**: Dispatched capacity from the previous time step.\n", + "- **Marginal Cost**: The current marginal cost of operating the agent's power-generating unit.\n", + "- **Previous Output**: The agent’s dispatched capacity (energy production) from the previous time step.\n", "\n", + "### Agent Actions\n", "\n", - "Agents choose actions based on the observations. The action space is two-dimensional, corresponding to:\n", + "The action space for the agents is two-dimensional and consists of:\n", "\n", - "- Bid Price for Inflexible Capacity (p_inflex): The price at which the agent offers its minimum power output (must-run capacity) to the market.\n", - "- Bid Price for Flexible Capacity (p_flex): The price for the additional capacity above the minimum output that the agent can flexibly adjust.\n", + "- **Bid Price for Inflexible Capacity (p_inflex)**: The price at which the agent offers its minimum power output (must-run capacity) to the market.\n", + "- **Bid Price for Flexible Capacity (p_flex)**: The price for the additional capacity above the minimum output that the agent can flexibly adjust.\n" + ] + }, + { + "cell_type": "markdown", + "id": "f3b17e49", + "metadata": { + "id": "e62e00c9" + }, + "source": [ + "#### 1.1.1 Install Assume and Required Packages\n", "\n", - "#### 1.1.1 Install Assume and needed Packages\n", + "In this section, we will install the necessary packages to run the **Assume framework** along with other dependencies.\n", + "The process is similar to the other tutorial on Assume.\n", "\n", - "Similar to the other tutorial, we can run Assume in the following way." + "The following commands will install Assume and its dependencies for reinforcement learning, along with additional libraries such as Plotly for visualization.\n", + "Make sure to install these before running the main code." ] }, { "cell_type": "code", - "execution_count": 1, - "id": "02dea28f", + "execution_count": null, + "id": "647079b9", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -153,15 +170,25 @@ }, "outputs": [], "source": [ - "#!pip install 'assume-framework[learning]'\n", - "#!pip install plotly\n", - "#!git clone https://github.com/assume-framework/assume.git assume-repo" + "!pip install 'assume-framework[learning]'\n", + "!pip install plotly\n", + "!git clone https://github.com/assume-framework/assume.git assume-repo" + ] + }, + { + "cell_type": "markdown", + "id": "a3ca95d2", + "metadata": { + "id": "e62e00c9" + }, + "source": [ + "You will also need to install additional optimization libraries like Pyomo and GLPK. These libraries are crucial for modeling and solving optimization problems in the simulation." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "544d38a4", + "execution_count": null, + "id": "7c8f0fb4", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -174,51 +201,41 @@ }, "outputs": [], "source": [ - "#!pip install pyomo\n", - "#!apt-get install -y -qq glpk-utils\n", - "#!pip install nbconvert" + "!pip install pyomo\n", + "!apt-get install -y -qq glpk-utils\n", + "!pip install nbconvert" ] }, { "cell_type": "markdown", - "id": "5ca60145", + "id": "e28add86", "metadata": {}, "source": [ - "Define paths to use depending on colab or local usage." + "Define paths to differentiate between Colab or local usage.\n", + "If you're running this on Google Colab, the paths might differ slightly from your local environment.\n", + "You can configure the paths accordingly based on where you're executing the code." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "a578e164", + "execution_count": null, + "id": "b7c91474", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bfd1daf2", - "outputId": "1edeb31f-bc3a-493e-b518-01f4188c44b6" + "id": "e62e00c9" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../inputs\n" - ] - } - ], + "outputs": [], "source": [ "import importlib.util\n", "\n", "import pandas as pd\n", "\n", - "# import plotly for visualization\n", + "#import plotly for visualization\n", "import plotly.graph_objects as go\n", "\n", - "# import yaml for reading and writing YAML files\n", + "#import yaml for reading and writing YAML files\n", "import yaml\n", "\n", - "# Check if 'google.colab' is available\n", + "#Check if 'google.colab' is available\n", "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", "\n", "colab_inputs_path = \"assume-repo/examples/inputs\"\n", @@ -231,43 +248,52 @@ }, { "cell_type": "markdown", - "id": "73087adf", + "id": "d95724ea", "metadata": { "id": "636ea9ae" }, "source": [ - "#### 1.1.2 Create and Load example files from market splitting tutorial\n", + "#### 1.1.2 Create and Load Example Files from Market Splitting Tutorial\n", + "\n", + "To define the RL Agent, we need to obtain the results from the **Market Zone Splitting** tutorial.\n", + "This tutorial provides essential data that the RL agent will use for decision-making.\n", "\n", - "We need to get the results form the market zone splitting tutorial, for which we are defining the RL Agent here. If you are working in colab execute the follwoing cells. If you are not working in colab but on your local machine simply open the respective tuttorial notebook and let it run." + "If you are working in **Google Colab**, execute the following cells to download and run the necessary notebook automatically. \n", + "If you are working on your **local machine**, simply open the respective tutorial notebook and execute it manually." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "116b9e37", + "execution_count": null, + "id": "85fdfe19", "metadata": { + "lines_to_next_cell": 2, "vscode": { "languageId": "shellscript" } }, "outputs": [], "source": [ - "# if used locally\n", - "#%cd assume/examples/notebooks/\n", + "# For local execution:\n", + "%cd assume/examples/notebooks/\n", "\n", - "# if used in colab\n", - "#%cd assume-repo/examples/notebooks/\n", + "# For execution in Google Colab:\n", + "%cd assume-repo/examples/notebooks/\n", "\n", - "#!jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output output.ipynb 08_market_zone_coupling.ipynb\n", + "# Execute the Market Zone Splitting tutorial:\n", + "!jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output output.ipynb 08_market_zone_coupling.ipynb\n", "\n", - "#%cd content\n", - "#!cp -r assume-repo/examples/notebooks/inputs ." + "# Return to content folder (for Colab):\n", + "%cd content\n", + "\n", + "# Copy inputs directory to the working folder (for Colab):\n", + "!cp -r assume-repo/examples/notebooks/inputs ." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "9871a2a5", + "execution_count": null, + "id": "1ca7eab9", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -275,15 +301,7 @@ "id": "233f315b", "outputId": "f98da7d4-0080-4546-c642-838f722965b0" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input CSV files have been read from 'inputs/tutorial_08'.\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -300,24 +318,24 @@ }, { "cell_type": "markdown", - "id": "bcfa21b7", + "id": "a9fc51a8", "metadata": { "id": "6985289b" }, "source": [ - "#### 1.1.3 Let's make this a leanring example\n", + "#### 1.1.3 Transform the Scenario into a Learning Example\n", "\n", - "The next cells show how we can transform any configured example in Assume into a learning example.\n", + "The following cells show how we can convert any pre-configured scenario in Assume into a learning example.\n", "\n", - "**Define a learning power plan**\n", + "**Define a Learning Power Plant**\n", "\n", - "We place a learning nuclear power plant in the south zone that has a 5 times hihger maximal power, to generate a scenario where it has a price impact." + "In this example, we place a learning nuclear power plant in the southern zone. This plant has five times the maximum power of a typical plant, which allows us to create a scenario where its actions have a noticeable impact on market prices." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "ac7d12fb", + "execution_count": null, + "id": "8c4153fa", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -326,311 +344,86 @@ "id": "b205256f", "outputId": "b9bb887b-f534-4a50-dd5b-229be1012600" }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
technologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
name
Unit 11nuclearnaive_eomuranium0.01000.00.00.315north_2Operator North
Unit 12nuclearnaive_eomuranium0.01000.00.00.316north_2Operator North
Unit 13nuclearnaive_eomuranium0.01000.00.00.317north_2Operator North
Unit 14nuclearnaive_eomuranium0.01000.00.00.318north_2Operator North
Unit 15nuclearnaive_eomuranium0.01000.00.00.319north_2Operator North
Unit 16nuclearnaive_eomuranium0.01000.00.00.320southOperator South
Unit 17nuclearnaive_eomuranium0.01000.00.00.321southOperator South
Unit 18nuclearnaive_eomuranium0.01000.00.00.322southOperator South
Unit 19nuclearnaive_eomuranium0.01000.00.00.323southOperator South
Unit 20nuclearpp_learninguranium0.05000.00.00.324southOperator-RL
\n", - "
" - ], - "text/plain": [ - " technology bidding_zonal fuel_type emission_factor max_power \\\n", - "name \n", - "Unit 11 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 12 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 13 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 14 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 15 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 16 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 17 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 18 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 19 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 20 nuclear pp_learning uranium 0.0 5000.0 \n", - "\n", - " min_power efficiency additional_cost node unit_operator \n", - "name \n", - "Unit 11 0.0 0.3 15 north_2 Operator North \n", - "Unit 12 0.0 0.3 16 north_2 Operator North \n", - "Unit 13 0.0 0.3 17 north_2 Operator North \n", - "Unit 14 0.0 0.3 18 north_2 Operator North \n", - "Unit 15 0.0 0.3 19 north_2 Operator North \n", - "Unit 16 0.0 0.3 20 south Operator South \n", - "Unit 17 0.0 0.3 21 south Operator South \n", - "Unit 18 0.0 0.3 22 south Operator South \n", - "Unit 19 0.0 0.3 23 south Operator South \n", - "Unit 20 0.0 0.3 24 south Operator-RL " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# create scarcity in south germany\n", + "# Create scarcity in southern Germany by limiting the number of power plants\n", "powerplant_units = powerplant_units[:20]\n", "\n", - "# assign RL power plant and give it market power\n", + "# Assign the RL-controlled power plant and give it market power\n", "powerplant_units.loc[19, \"bidding_zonal\"] = \"pp_learning\"\n", - "powerplant_units.loc[19, \"max_power\"] = 5000\n", + "powerplant_units.loc[19, \"max_power\"] = 5000 # Set maximum power to 5000 MW\n", "\n", - "# assig specific RL unit operator to plant\n", + "# Assign a specific RL unit operator to the plant\n", "powerplant_units.loc[19, \"unit_operator\"] = \"Operator-RL\"\n", "\n", - "# make name column to index\n", + "# Set the 'name' column as the index\n", "powerplant_units.set_index(\"name\", inplace=True, drop=True)\n", "\n", - "# store power plant units to csv again\n", + "# Save the updated power plant units to a CSV file\n", "powerplant_units.to_csv(input_dir + \"/powerplant_units.csv\")\n", "\n", + "# Show the last 10 entries\n", "powerplant_units.tail(10)" ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9316ac03", - "metadata": { - "id": "QBTGrw62_5I7" - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "id": "8f995ab9", + "id": "c8a22a61", "metadata": { "id": "cce0e8b4" }, "source": [ "**Configure Learning Hyperparameters in YAML**\n", "\n", - "Change the yaml to configure the learning specific hyperparameters. In the following we provide a brief description of the hyper parameters:\n", + "The following YAML configuration contains the learning-specific hyperparameters that will guide the RL agent's training process. Below is a brief description of these hyperparameters:\n", "\n", "- **continue_learning** (`False`): \n", - " - Indicates whether the agent should continue training from a previously saved state or start fresh.\n", + " - Whether to continue training from a previously saved state or start fresh.\n", "\n", "- **max_bid_price** (`100`): \n", - " - The maximum bid price allowed during the agent's interaction with the environment, which is used to scale the output of the actor.\n", + " - The maximum allowable bid price for the agent, used to scale the actor's output.\n", "\n", "- **algorithm** (`\"matd3\"`): \n", - " - The type of reinforcement learning algorithm used. In this case, `MATD3`, which stands for Multi-Agent Twin Delayed Deep Deterministic Policy Gradient.\n", + " - The learning algorithm to be used, in this case `MATD3` (Multi-Agent Twin Delayed Deep Deterministic Policy Gradient).\n", "\n", "- **learning_rate** (`0.001`): \n", - " - The learning rate for the algorithm's optimizer. This determines how big the steps of the models parameter update are during training.\n", + " - The rate at which the model’s parameters are updated during training.\n", "\n", "- **training_episodes** (`50`): \n", - " - The total number of training episodes the agent will go through.\n", + " - The total number of episodes for training the agent.\n", "\n", "- **episodes_collecting_initial_experience** (`3`): \n", - " - The number of episodes dedicated to collecting initial experience before training begins. During this period the agent follows a random policy around some base value.\n", + " - Number of episodes dedicated to collecting initial experience before actual training begins, during which the agent follows a random policy.\n", "\n", - "- **train_freq** (`\"24h\"`): \n", - " - The frequency at which the model is trained. In this case, training occurs every 24 hours.\n", + "- **train_freq** (`\"4h\"`): \n", + " - Frequency of model training, in this case, every 4 hours.\n", "\n", "- **gradient_steps** (`-1`): \n", - " - The number of gradient steps to be taken at each training interval. A value of `-1` typically means to perform as many gradient steps as the number of collected experience samples allows.\n", + " - The number of gradient updates to perform at each training step. A value of `-1` typically means that all collected experience will be used for training.\n", "\n", "- **batch_size** (`256`): \n", - " - The number of samples in each mini-batch used for training the model.\n", + " - The size of the mini-batch used for training.\n", "\n", "- **gamma** (`0.99`): \n", - " - The discount factor for future rewards, representing how much importance the agent places on long-term rewards versus immediate rewards.\n", + " - The discount factor for future rewards, balancing short-term vs. long-term reward importance.\n", "\n", "- **device** (`\"cpu\"`): \n", - " - The computational device used for training. In this case, training is performed on a CPU.\n", + " - The computational device for training. In this case, the CPU is used.\n", "\n", "- **noise_sigma** (`0.1`): \n", - " - The standard deviation (sigma) of the noise added to the actions for exploration purposes.\n", + " - The standard deviation of the exploration noise added to actions.\n", "\n", - "- **noise_scale** (`1`): \n", - "- **noise_dt** (`1`): \n", - " - A scaling factor applied to the noise added for exploration, influencing the amount of exploration.\n", - " - it is used for decay, but since both values are 1 no decay is applied. \n", + "- **noise_scale** (`1`) and **noise_dt** (`1`): \n", + " - Parameters controlling the scale and time step of the exploration noise. Since both are set to 1, no decay is applied.\n", "\n", - "- **validation_episodes_interval** (`5`): \n", - " - The interval at which the model is validated during training, i.e., validation occurs every 5 episodes.\n" + "- **validation_episodes_interval** (`3`): \n", + " - The interval (in episodes) at which validation is performed during training." ] }, { "cell_type": "code", - "execution_count": 53, - "id": "03cc1a12", + "execution_count": null, + "id": "f6c64dc2", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -638,16 +431,9 @@ "id": "9c555ce9", "outputId": "473126ae-3c3e-4698-e3a5-347cc00e5108" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Configuration YAML file has been saved to '../inputs\\tutorial_08\\config.yaml'.\n" - ] - } - ], + "outputs": [], "source": [ + "# YAML configuration for the RL training\n", "config = {\n", " \"zonal_case\": {\n", " \"start_date\": \"2019-01-01 00:00\",\n", @@ -692,7 +478,7 @@ " }\n", "}\n", "\n", - "# Define the path for the config file\n", + "# Define the path for the configuration file\n", "config_path = os.path.join(input_dir, \"config.yaml\")\n", "\n", "# Save the configuration to a YAML file\n", @@ -704,18 +490,19 @@ }, { "cell_type": "markdown", - "id": "59a880d8", + "id": "052cbdb4", "metadata": { "id": "3f0f38fb" }, "source": [ - "For XRL, we need enhanced logging of the learning process, which is not currently a feature of ASSUME itself. Therefore, we are overriding some functions to enable this logging specifically for the purpose of this tutorial." + "In order to make this setup compatible with XRL, we need to enhance the logging of the learning process. \n", + "ASSUME does not have this feature natively, so we will override some functions to enable this logging for the purpose of this tutorial." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "823db62a", + "execution_count": null, + "id": "a01977d5", "metadata": { "cellView": "form", "id": "201251c6" @@ -927,20 +714,21 @@ }, { "cell_type": "markdown", - "id": "d52cb7d4", + "id": "29fa6b82", "metadata": { "id": "dcacfe26" }, "source": [ - "**Run the example case**\n", + "**Run the Example Case**\n", "\n", - "Now we run the example case similar to before in the market zone tutorial. the only difference is that we call the run_learning function, whcih itterates multiple times over the simulation horizon. " + "Now we run the example case as done previously in the market zone tutorial. \n", + "The main difference here is that we call the `run_learning()` function, which iterates multiple times over the simulation horizon for reinforcement learning." ] }, { "cell_type": "code", - "execution_count": 54, - "id": "9092a097", + "execution_count": null, + "id": "0c1c9334", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -949,1546 +737,36 @@ "id": "bfadf522", "outputId": "7c91ab13-a3c2-4e89-d8ac-d20be95391f6" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from ../inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801.0it [00:06, 12013.00it/s]\n", - "Training Episodes: 2%|▏ | 1/50 [00:06<05:42, 7.00s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_2 2019-01-01 23:00:00: : 82801.0it [00:06, 12345.61it/s]\n", - "Training Episodes: 4%|▍ | 2/50 [00:13<05:31, 6.91s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_3 2019-01-01 23:00:00: : 82801.0it [00:05, 15499.94it/s]\n", - "Training Episodes: 6%|▌ | 3/50 [00:19<04:54, 6.26s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_4 2019-01-01 23:00:00: : 82801.0it [00:07, 11198.80it/s]\n", - "Training Episodes: 8%|▊ | 4/50 [00:26<05:10, 6.75s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_5 2019-01-01 23:00:00: : 82801.0it [00:06, 13713.80it/s]\n", - "Training Episodes: 10%|█ | 5/50 [00:32<04:53, 6.52s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_6 2019-01-01 23:00:00: : 82801.0it [00:05, 14446.60it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "tutorial_08_zonal_case_eval_1 2019-01-01 23:00:00: : 82801.0it [00:05, 15472.29it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 1, metric='avg_reward', value=4469.33\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 12%|█▏ | 6/50 [00:44<05:58, 8.14s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_7 2019-01-01 23:00:00: : 82801.0it [00:06, 12253.09it/s]\n", - "Training Episodes: 14%|█▍ | 7/50 [00:51<05:32, 7.72s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_8 2019-01-01 23:00:00: : 82801.0it [00:05, 14559.37it/s]\n", - "Training Episodes: 16%|█▌ | 8/50 [00:56<04:58, 7.11s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_9 2019-01-01 23:00:00: : 82801.0it [00:05, 13944.09it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "tutorial_08_zonal_case_eval_2 2019-01-01 23:00:00: : 82801.0it [00:06, 12881.54it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 2, metric='avg_reward', value=4474.74\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 18%|█▊ | 9/50 [01:09<06:02, 8.84s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_10 2019-01-01 23:00:00: : 82801.0it [00:07, 11479.97it/s]\n", - "Training Episodes: 20%|██ | 10/50 [01:16<05:35, 8.38s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_11 2019-01-01 23:00:00: : 82801.0it [00:06, 13599.80it/s]\n", - "Training Episodes: 22%|██▏ | 11/50 [01:23<05:00, 7.72s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_12 2019-01-01 23:00:00: : 82801.0it [00:06, 11938.13it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "tutorial_08_zonal_case_eval_3 2019-01-01 23:00:00: : 82801.0it [00:06, 12805.93it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 3, metric='avg_reward', value=4475.47\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 24%|██▍ | 12/50 [01:36<06:02, 9.53s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_13 2019-01-01 23:00:00: : 82801.0it [00:05, 14116.94it/s]\n", - "Training Episodes: 26%|██▌ | 13/50 [01:42<05:13, 8.46s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_14 2019-01-01 23:00:00: : 82801.0it [00:07, 11604.06it/s]\n", - "Training Episodes: 28%|██▊ | 14/50 [01:50<04:51, 8.10s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_15 2019-01-01 23:00:00: : 82801.0it [00:06, 13097.26it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_eval_4 2019-01-01 23:00:00: : 82801.0it [00:05, 13963.28it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 4, metric='avg_reward', value=4475.63\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 30%|███ | 15/50 [02:02<05:29, 9.43s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_16 2019-01-01 23:00:00: : 82801.0it [00:07, 11624.53it/s]\n", - "Training Episodes: 32%|███▏ | 16/50 [02:09<04:57, 8.76s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_17 2019-01-01 23:00:00: : 82801.0it [00:05, 14092.96it/s]\n", - "Training Episodes: 34%|███▍ | 17/50 [02:15<04:21, 7.93s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_18 2019-01-01 23:00:00: : 82801.0it [00:06, 12444.20it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "tutorial_08_zonal_case_eval_5 2019-01-01 23:00:00: : 82801.0it [00:05, 15771.05it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 5, metric='avg_reward', value=4475.69\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 36%|███▌ | 18/50 [02:27<04:53, 9.19s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_19 2019-01-01 23:00:00: : 82801.0it [00:05, 13997.61it/s]\n", - "Training Episodes: 38%|███▊ | 19/50 [02:33<04:15, 8.24s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_20 2019-01-01 23:00:00: : 82801.0it [00:06, 11920.26it/s]\n", - "Training Episodes: 40%|████ | 20/50 [02:41<03:56, 7.90s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_21 2019-01-01 23:00:00: : 82801.0it [00:06, 13709.91it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "tutorial_08_zonal_case_eval_6 2019-01-01 23:00:00: : 82801.0it [00:06, 13764.76it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 6, metric='avg_reward', value=4475.75\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 42%|████▏ | 21/50 [02:53<04:27, 9.22s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_22 2019-01-01 23:00:00: : 82801.0it [00:05, 14997.12it/s]\n", - "Training Episodes: 44%|████▍ | 22/50 [02:58<03:48, 8.15s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_23 2019-01-01 23:00:00: : 82801.0it [00:05, 14215.35it/s]\n", - "Training Episodes: 46%|████▌ | 23/50 [03:04<03:22, 7.49s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_24 2019-01-01 23:00:00: : 82801.0it [00:06, 13508.71it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_eval_7 2019-01-01 23:00:00: : 82801.0it [00:06, 12904.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 7, metric='avg_reward', value=4475.79\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 48%|████▊ | 24/50 [03:17<03:56, 9.09s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_25 2019-01-01 23:00:00: : 82801.0it [00:07, 11300.03it/s]\n", - "Training Episodes: 50%|█████ | 25/50 [03:25<03:34, 8.60s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_26 2019-01-01 23:00:00: : 82801.0it [00:05, 14158.58it/s]\n", - "Training Episodes: 52%|█████▏ | 26/50 [03:31<03:07, 7.82s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_27 2019-01-01 23:00:00: : 82801.0it [00:06, 13228.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_eval_8 2019-01-01 23:00:00: : 82801.0it [00:05, 16426.27it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 8, metric='avg_reward', value=4475.83\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 54%|█████▍ | 27/50 [03:42<03:25, 8.94s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_28 2019-01-01 23:00:00: : 82801.0it [00:06, 12827.07it/s]\n", - "Training Episodes: 56%|█████▌ | 28/50 [03:49<03:01, 8.23s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_29 2019-01-01 23:00:00: : 82801.0it [00:05, 13859.42it/s]\n", - "Training Episodes: 58%|█████▊ | 29/50 [03:55<02:39, 7.60s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_30 2019-01-01 23:00:00: : 82801.0it [00:05, 14440.04it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_eval_9 2019-01-01 23:00:00: : 82801.0it [00:06, 13450.67it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 9, metric='avg_reward', value=4475.87\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 60%|██████ | 30/50 [04:07<02:59, 8.96s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_31 2019-01-01 23:00:00: : 82801.0it [00:06, 12824.65it/s]\n", - "Training Episodes: 62%|██████▏ | 31/50 [04:14<02:36, 8.24s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_32 2019-01-01 23:00:00: : 82801.0it [00:06, 12637.94it/s]\n", - "Training Episodes: 64%|██████▍ | 32/50 [04:20<02:19, 7.77s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_33 2019-01-01 23:00:00: : 82801.0it [00:05, 14130.55it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_eval_10 2019-01-01 23:00:00: : 82801.0it [00:06, 13407.73it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 10, metric='avg_reward', value=4475.90\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 66%|██████▌ | 33/50 [04:33<02:35, 9.12s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_34 2019-01-01 23:00:00: : 82801.0it [00:07, 11755.06it/s]\n", - "Training Episodes: 68%|██████▊ | 34/50 [04:40<02:16, 8.55s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_35 2019-01-01 23:00:00: : 82801.0it [00:05, 14936.66it/s]\n", - "Training Episodes: 70%|███████ | 35/50 [04:45<01:55, 7.68s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_36 2019-01-01 23:00:00: : 82801.0it [00:07, 11130.25it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "tutorial_08_zonal_case_eval_11 2019-01-01 23:00:00: : 82801.0it [00:06, 11935.15it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 11, metric='avg_reward', value=4475.93\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 72%|███████▏ | 36/50 [05:00<02:17, 9.79s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_37 2019-01-01 23:00:00: : 82801.0it [00:06, 12797.34it/s]\n", - "Training Episodes: 74%|███████▍ | 37/50 [05:07<01:54, 8.83s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_38 2019-01-01 23:00:00: : 82801.0it [00:06, 12915.06it/s]\n", - "Training Episodes: 76%|███████▌ | 38/50 [05:13<01:37, 8.15s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_39 2019-01-01 23:00:00: : 82801.0it [00:06, 12850.67it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_eval_12 2019-01-01 23:00:00: : 82801.0it [00:06, 13529.14it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 12, metric='avg_reward', value=4475.95\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 78%|███████▊ | 39/50 [05:26<01:45, 9.56s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_40 2019-01-01 23:00:00: : 82801.0it [00:06, 12461.16it/s]\n", - "Training Episodes: 80%|████████ | 40/50 [05:33<01:27, 8.73s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_41 2019-01-01 23:00:00: : 82801.0it [00:05, 14490.69it/s]\n", - "Training Episodes: 82%|████████▏ | 41/50 [05:39<01:10, 7.85s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_42 2019-01-01 23:00:00: : 82801.0it [00:05, 14582.93it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "tutorial_08_zonal_case_eval_13 2019-01-01 23:00:00: : 82801.0it [00:06, 13318.90it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 13, metric='avg_reward', value=4475.98\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Training Episodes: 84%|████████▍ | 42/50 [05:51<01:13, 9.15s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_43 2019-01-01 23:00:00: : 82801.0it [00:07, 11473.78it/s]\n", - "Training Episodes: 86%|████████▌ | 43/50 [05:58<01:00, 8.62s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_44 2019-01-01 23:00:00: : 82801.0it [00:05, 14519.97it/s]\n", - "Training Episodes: 88%|████████▊ | 44/50 [06:04<00:46, 7.77s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_45 2019-01-01 23:00:00: : 82801.0it [00:06, 13424.52it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "tutorial_08_zonal_case_eval_14 2019-01-01 23:00:00: : 82801.0it [00:06, 12595.71it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 14, metric='avg_reward', value=4476.00\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 90%|█████████ | 45/50 [06:17<00:46, 9.35s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_46 2019-01-01 23:00:00: : 82801.0it [00:07, 11662.90it/s]\n", - "Training Episodes: 92%|█████████▏| 46/50 [06:24<00:34, 8.71s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_47 2019-01-01 23:00:00: : 82801.0it [00:06, 13631.36it/s]\n", - "Training Episodes: 94%|█████████▍| 47/50 [06:31<00:23, 7.96s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_48 2019-01-01 23:00:00: : 82801.0it [00:06, 13672.99it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_eval_15 2019-01-01 23:00:00: : 82801.0it [00:04, 16605.74it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 15, metric='avg_reward', value=4476.02\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 96%|█████████▌| 48/50 [06:42<00:17, 8.95s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_49 2019-01-01 23:00:00: : 82801.0it [00:07, 11704.20it/s]\n", - "Training Episodes: 98%|█████████▊| 49/50 [06:49<00:08, 8.42s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_50 2019-01-01 23:00:00: : 82801.0it [00:06, 13377.79it/s]\n", - "Training Episodes: 100%|██████████| 50/50 [07:18<00:00, 8.76s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/82800 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
price forecast t+1price forecast t+2price forecast t+3price forecast t+4price forecast t+5price forecast t+6price forecast t+7price forecast t+8price forecast t+9price forecast t+10...residual load forecast t+17residual load forecast t+18residual load forecast t+19residual load forecast t+20residual load forecast t+21residual load forecast t+22residual load forecast t+23residual load forecast t+24total capacity t-1marginal costs t-1
02.242.262.282.302.322.342.362.382.402.42...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.4066670.000.406667
12.262.282.302.322.342.362.382.402.422.44...0.0000000.0000000.0000000.0000000.0000000.0000000.4066670.4066670.680.406667
22.282.302.322.342.362.382.402.422.442.46...0.0000000.0000000.0000000.0000000.0000000.4066670.4066670.4066670.720.406667
32.302.322.342.362.382.402.422.442.462.48...0.0000000.0000000.0000000.0000000.4066670.4066670.4066670.4066670.760.406667
42.322.342.362.382.402.422.442.462.482.50...0.0000000.0000000.0000000.4066670.4066670.4066670.4066670.4066671.000.406667
..................................................................
6952.462.482.502.522.542.562.582.602.622.64...0.4066670.4066670.4066670.4066670.4066670.0000000.0000000.0000001.000.406667
6962.482.502.522.542.562.582.602.622.642.66...0.4066670.4066670.4066670.4066670.0000000.0000000.0000000.0000001.000.406667
6972.502.522.542.562.582.602.622.642.662.68...0.4066670.4066670.4066670.0000000.0000000.0000000.0000000.0000001.000.406667
6982.522.542.562.582.602.622.642.662.682.22...0.4066670.4066670.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
6992.542.562.582.602.622.642.662.682.222.24...0.4066670.0000000.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
\n", - "

700 rows × 50 columns

\n", - "" - ], - "text/plain": [ - " price forecast t+1 price forecast t+2 price forecast t+3 \\\n", - "0 2.24 2.26 2.28 \n", - "1 2.26 2.28 2.30 \n", - "2 2.28 2.30 2.32 \n", - "3 2.30 2.32 2.34 \n", - "4 2.32 2.34 2.36 \n", - ".. ... ... ... \n", - "695 2.46 2.48 2.50 \n", - "696 2.48 2.50 2.52 \n", - "697 2.50 2.52 2.54 \n", - "698 2.52 2.54 2.56 \n", - "699 2.54 2.56 2.58 \n", - "\n", - " price forecast t+4 price forecast t+5 price forecast t+6 \\\n", - "0 2.30 2.32 2.34 \n", - "1 2.32 2.34 2.36 \n", - "2 2.34 2.36 2.38 \n", - "3 2.36 2.38 2.40 \n", - "4 2.38 2.40 2.42 \n", - ".. ... ... ... \n", - "695 2.52 2.54 2.56 \n", - "696 2.54 2.56 2.58 \n", - "697 2.56 2.58 2.60 \n", - "698 2.58 2.60 2.62 \n", - "699 2.60 2.62 2.64 \n", - "\n", - " price forecast t+7 price forecast t+8 price forecast t+9 \\\n", - "0 2.36 2.38 2.40 \n", - "1 2.38 2.40 2.42 \n", - "2 2.40 2.42 2.44 \n", - "3 2.42 2.44 2.46 \n", - "4 2.44 2.46 2.48 \n", - ".. ... ... ... \n", - "695 2.58 2.60 2.62 \n", - "696 2.60 2.62 2.64 \n", - "697 2.62 2.64 2.66 \n", - "698 2.64 2.66 2.68 \n", - "699 2.66 2.68 2.22 \n", - "\n", - " price forecast t+10 ... residual load forecast t+17 \\\n", - "0 2.42 ... 0.000000 \n", - "1 2.44 ... 0.000000 \n", - "2 2.46 ... 0.000000 \n", - "3 2.48 ... 0.000000 \n", - "4 2.50 ... 0.000000 \n", - ".. ... ... ... \n", - "695 2.64 ... 0.406667 \n", - "696 2.66 ... 0.406667 \n", - "697 2.68 ... 0.406667 \n", - "698 2.22 ... 0.406667 \n", - "699 2.24 ... 0.406667 \n", - "\n", - " residual load forecast t+18 residual load forecast t+19 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.000000 \n", - "4 0.000000 0.000000 \n", - ".. ... ... \n", - "695 0.406667 0.406667 \n", - "696 0.406667 0.406667 \n", - "697 0.406667 0.406667 \n", - "698 0.406667 0.000000 \n", - "699 0.000000 0.000000 \n", - "\n", - " residual load forecast t+20 residual load forecast t+21 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.406667 \n", - "4 0.406667 0.406667 \n", - ".. ... ... \n", - "695 0.406667 0.406667 \n", - "696 0.406667 0.000000 \n", - "697 0.000000 0.000000 \n", - "698 0.000000 0.000000 \n", - "699 0.000000 0.000000 \n", - "\n", - " residual load forecast t+22 residual load forecast t+23 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.406667 \n", - "2 0.406667 0.406667 \n", - "3 0.406667 0.406667 \n", - "4 0.406667 0.406667 \n", - ".. ... ... \n", - "695 0.000000 0.000000 \n", - "696 0.000000 0.000000 \n", - "697 0.000000 0.000000 \n", - "698 0.000000 0.000000 \n", - "699 0.000000 0.000000 \n", - "\n", - " residual load forecast t+24 total capacity t-1 marginal costs t-1 \n", - "0 0.406667 0.00 0.406667 \n", - "1 0.406667 0.68 0.406667 \n", - "2 0.406667 0.72 0.406667 \n", - "3 0.406667 0.76 0.406667 \n", - "4 0.406667 1.00 0.406667 \n", - ".. ... ... ... \n", - "695 0.000000 1.00 0.406667 \n", - "696 0.000000 1.00 0.406667 \n", - "697 0.000000 1.00 0.406667 \n", - "698 0.000000 1.00 0.406667 \n", - "699 0.000000 1.00 0.406667 \n", - "\n", - "[700 rows x 50 columns]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# path to extra loggedobservation values \n", "path = (\n", @@ -4300,7 +1167,7 @@ }, { "cell_type": "markdown", - "id": "509b19bf", + "id": "8add0715", "metadata": { "id": "5d8b9dcf" }, @@ -4310,7 +1177,7 @@ }, { "cell_type": "markdown", - "id": "bf2b101a", + "id": "d7a9e67d", "metadata": { "id": "b1b50488" }, @@ -4320,8 +1187,8 @@ }, { "cell_type": "code", - "execution_count": 62, - "id": "c27178fe", + "execution_count": null, + "id": "cca85e13", "metadata": { "id": "4da4de57" }, @@ -4337,23 +1204,12 @@ }, { "cell_type": "code", - "execution_count": 63, - "id": "35a088d5", + "execution_count": null, + "id": "1cd3b7e6", "metadata": { "id": "37adecfa" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# which actor is the RL actor\n", "ACTOR_NUM = len(powerplant_units) # 20\n", @@ -4371,7 +1227,7 @@ }, { "cell_type": "markdown", - "id": "fe43c9c6", + "id": "85d43a1b", "metadata": { "id": "d4a63712" }, @@ -4381,8 +1237,8 @@ }, { "cell_type": "code", - "execution_count": 64, - "id": "4ee77fed", + "execution_count": null, + "id": "c507d331", "metadata": { "id": "e6460cfb" }, @@ -4397,7 +1253,7 @@ }, { "cell_type": "markdown", - "id": "633c5100", + "id": "579665bc", "metadata": { "id": "ddd1ab1e" }, @@ -4411,19 +1267,10 @@ }, { "cell_type": "code", - "execution_count": 65, - "id": "e5738928", + "execution_count": null, + "id": "b0758eb5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n" - ] - } - ], + "outputs": [], "source": [ "# @ Title Split the data into training and testing sets\n", "X_train, X_test, y_train, y_test = train_test_split(\n", @@ -4442,7 +1289,7 @@ }, { "cell_type": "markdown", - "id": "c278a8c6", + "id": "abbaa45f", "metadata": { "id": "ae7b108b", "lines_to_next_cell": 2 @@ -4453,8 +1300,8 @@ }, { "cell_type": "code", - "execution_count": 66, - "id": "77b37ebc", + "execution_count": null, + "id": "40e12192", "metadata": { "id": "6d9be211" }, @@ -4470,20 +1317,12 @@ }, { "cell_type": "code", - "execution_count": 67, - "id": "1eac5396", + "execution_count": null, + "id": "56a32f41", "metadata": { "id": "84bb96cf" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:shap:Using 595 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n" - ] - } - ], + "outputs": [], "source": [ "# Create the SHAP Kernel Explainer\n", "explainer = shap.KernelExplainer(model_predict, X_train)" @@ -4491,5270 +1330,12 @@ }, { "cell_type": "code", - "execution_count": 68, - "id": "c38544fd", + "execution_count": null, + "id": "4279910b", "metadata": { "id": "2a7929e4" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/105 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Summary plot for the first output dimension\n", "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", @@ -9873,7 +1399,7 @@ }, { "cell_type": "markdown", - "id": "bd527a94", + "id": "7ee7ebfb", "metadata": { "id": "9a888f8b" }, @@ -9883,7 +1409,7 @@ }, { "cell_type": "markdown", - "id": "3244d847", + "id": "555afa84", "metadata": { "id": "c6c4ce8c" }, @@ -9894,7 +1420,7 @@ }, { "cell_type": "markdown", - "id": "37dc7965", + "id": "5dc307ed", "metadata": { "id": "86545200" }, @@ -9904,7 +1430,7 @@ }, { "cell_type": "markdown", - "id": "294524a4", + "id": "740b57e2", "metadata": { "id": "06f3977c" }, @@ -9914,7 +1440,7 @@ }, { "cell_type": "markdown", - "id": "ba535ae6", + "id": "0f79634a", "metadata": { "id": "dadd0a0c" }, @@ -9924,7 +1450,7 @@ }, { "cell_type": "markdown", - "id": "1282f757", + "id": "d0035820", "metadata": { "id": "37633c16" }, @@ -9934,7 +1460,7 @@ }, { "cell_type": "markdown", - "id": "731058ed", + "id": "80c10373", "metadata": { "id": "8735d66f" }, @@ -9944,7 +1470,7 @@ }, { "cell_type": "markdown", - "id": "9b8fb14b", + "id": "80754bea", "metadata": { "id": "d6b0332f" }, @@ -9957,7 +1483,7 @@ }, { "cell_type": "markdown", - "id": "3c17d271", + "id": "0b873097", "metadata": { "id": "a8cdea5f" }, @@ -9968,26 +1494,12 @@ ], "metadata": { "jupytext": { - "cell_metadata_filter": "vscode,id,outputId,cellView,colab,-all", + "cell_metadata_filter": "vscode,outputId,colab,cellView,id,-all", "main_language": "python", "notebook_metadata_filter": "-all" }, - "kernelspec": { - "display_name": "assume-framework", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" + "name": "python" } }, "nbformat": 4,