joefarrington
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dqn-SpaceInvadersNoFrameskip-v4.zip
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dqn-SpaceInvadersNoFrameskip-v4/data
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"__module__": "stable_baselines3.dqn.policies",
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"__doc__": "\n Policy class for DQN when using images as input.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param features_extractor_class: Features extractor to use.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
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"__init__": "<function CnnPolicy.__init__ at
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"__module__": "stable_baselines3.common.buffers",
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"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device:\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
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"__module__": "stable_baselines3.dqn.policies",
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"__doc__": "\n Policy class for DQN when using images as input.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param features_extractor_class: Features extractor to use.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
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"__module__": "stable_baselines3.common.buffers",
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"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device:\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
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"train_freq": {
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"exploration_rate": 0.01,
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dqn-SpaceInvadersNoFrameskip-v4/system_info.txt
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OS: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.
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Python: 3.8.
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Stable-Baselines3: 1.5.1a8
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PyTorch: 1.11.0
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GPU Enabled: True
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OS: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.10 #1 SMP Fri Apr 2 22:23:49 UTC 2021
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Python: 3.8.5
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Stable-Baselines3: 1.5.1a8
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PyTorch: 1.11.0
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GPU Enabled: True
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replay.mp4
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results.json
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{"mean_reward": 758.0, "std_reward": 206.44854080375575, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2022-06-
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{"mean_reward": 758.0, "std_reward": 206.44854080375575, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2022-06-23T18:56:38.779409"}
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