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pygame 1.9.4
Hello from the pygame community. https://www.pygame.org/contribute.html
Loading chipmunk for Linux (64bit) [/home/deep7/hierarchy/local/lib/python2.7/site-packages/pymunk/libchipmunk64.so]
Initializing cpSpace - Chipmunk v6.2.0 (Debug Enabled)
Compile with -DNDEBUG defined to disable debug mode and runtime assertion checks
[33mWARN: Environment '<class 'gym_cars.envs.environment.carsEnv'>' has deprecated methods '_step' and '_reset' rather than 'step' and 'reset'. Compatibility code invoked. Set _gym_disable_underscore_compat = True to disable this behavior.[0m
2019-02-27 11:37:42.466327: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-02-27 11:37:42.553140: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-02-27 11:37:42.553498: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.8095
pciBusID: 0000:01:00.0
totalMemory: 7.93GiB freeMemory: 7.44GiB
2019-02-27 11:37:42.553513: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-02-27 11:37:42.765376: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-02-27 11:37:42.765411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-02-27 11:37:42.765417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-02-27 11:37:42.765559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7179 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
I0227 11:37:42.767091 140522456590144 tf_logging.py:115] Creating DQNAgent agent with the following parameters:
I0227 11:37:42.767338 140522456590144 tf_logging.py:115] gamma: 0.990000
I0227 11:37:42.767391 140522456590144 tf_logging.py:115] update_horizon: 1.000000
I0227 11:37:42.767435 140522456590144 tf_logging.py:115] min_replay_history: 50000
I0227 11:37:42.767474 140522456590144 tf_logging.py:115] update_period: 1
I0227 11:37:42.767513 140522456590144 tf_logging.py:115] target_update_period: 1000
I0227 11:37:42.767549 140522456590144 tf_logging.py:115] epsilon_train: 0.010000
I0227 11:37:42.767585 140522456590144 tf_logging.py:115] epsilon_eval: 0.001000
I0227 11:37:42.767621 140522456590144 tf_logging.py:115] epsilon_decay_period: 1000000
I0227 11:37:42.767658 140522456590144 tf_logging.py:115] tf_device: /gpu:0
I0227 11:37:42.767693 140522456590144 tf_logging.py:115] use_staging: True
I0227 11:37:42.767729 140522456590144 tf_logging.py:115] optimizer: <tensorflow.python.training.rmsprop.RMSPropOptimizer object at 0x7fcd517b5490>
I0227 11:37:42.769056 140522456590144 tf_logging.py:115] Creating a OutOfGraphReplayBuffer replay memory with the following parameters:
I0227 11:37:42.769130 140522456590144 tf_logging.py:115] observation_shape: (84, 84)
I0227 11:37:42.769181 140522456590144 tf_logging.py:115] observation_dtype: <type 'numpy.uint8'>
I0227 11:37:42.769223 140522456590144 tf_logging.py:115] stack_size: 4
I0227 11:37:42.769263 140522456590144 tf_logging.py:115] replay_capacity: 1000000
I0227 11:37:42.769311 140522456590144 tf_logging.py:115] batch_size: 32
I0227 11:37:42.769350 140522456590144 tf_logging.py:115] update_horizon: 1
I0227 11:37:42.769387 140522456590144 tf_logging.py:115] gamma: 0.990000