araffin commited on
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dd1f7df
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README.md CHANGED
@@ -10,7 +10,7 @@ model-index:
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  results:
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  - metrics:
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  - type: mean_reward
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- value: 256.40 +/- 20.75
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  name: mean_reward
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  task:
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  type: reinforcement-learning
 
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  results:
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  - metrics:
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  - type: mean_reward
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+ value: 280.22 +/- 13.03
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  name: mean_reward
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  task:
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  type: reinforcement-learning
config.json CHANGED
@@ -1 +1 @@
1
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  },
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  "ep_success_buffer": {
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  },
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- "_n_updates": 399996,
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- "buffer_size": 50000,
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  "batch_size": 128,
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  "learning_starts": 0,
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  "tau": 1.0,
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  "gamma": 0.99,
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- "gradient_steps": -1,
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  "optimize_memory_usage": false,
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  "replay_buffer_class": {
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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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- "__init__": "<function ReplayBuffer.__init__ at 0x7ff4901f8200>",
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- "sample": "<function ReplayBuffer.sample at 0x7ff4901efb90>",
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  },
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  "replay_buffer_kwargs": {},
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  "train_freq": {
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  },
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  "actor": null,
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  "use_sde_at_warmup": false,
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  "exploration_initial_eps": 1.0,
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- "exploration_final_eps": 0.1,
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- "exploration_fraction": 0.02,
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- "target_update_interval": 250,
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- "_n_calls": 399999,
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  "max_grad_norm": 10,
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- "exploration_rate": 0.1,
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  "exploration_schedule": {
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  ":type:": "<class 'function'>",
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  }
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  }
 
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  "__module__": "stable_baselines3.dqn.policies",
6
  "__doc__": "\n Policy class with Q-Value Net and target net for DQN\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 features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\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 ",
7
+ "__init__": "<function DQNPolicy.__init__ at 0x7f7ae0b72b90>",
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+ "_build": "<function DQNPolicy._build at 0x7f7ae0b72c20>",
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+ "make_q_net": "<function DQNPolicy.make_q_net at 0x7f7ae0b72cb0>",
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+ "forward": "<function DQNPolicy.forward at 0x7f7ae0b72d40>",
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+ "_predict": "<function DQNPolicy._predict at 0x7f7ae0b72dd0>",
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+ "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f7ae0b72e60>",
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+ "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f7ae0b72ef0>",
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  "__abstractmethods__": "frozenset()",
15
+ "_abc_impl": "<_abc_data object at 0x7f7ae0b5d990>"
16
  },
17
  "verbose": 1,
18
  "policy_kwargs": {
 
36
  },
37
  "action_space": {
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