Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
  type: LunarLander-v2
  metrics:
  - type: mean_reward
- value: 267.75 +/- 19.66
+ value: 273.73 +/- 16.85
  name: mean_reward
  verified: false
 ---

config.json

@@ -1 +1 @@
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It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7d30ea9e1630>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d30ea9e16c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d30ea9e1750>", 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It allows to keep variance\n above zero and prevent it from growing too fast. 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  "__module__": "stable_baselines3.common.policies",
  "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\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 ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 ",
- "__init__": "<function ActorCriticPolicy.__init__ at 0x7d30ea9e1630>",
- "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d30ea9e16c0>",
- "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d30ea9e1750>",
- "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d30ea9e17e0>",
- "_build": "<function ActorCriticPolicy._build at 0x7d30ea9e1870>",
- "forward": "<function ActorCriticPolicy.forward at 0x7d30ea9e1900>",
- "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d30ea9e1990>",
- "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d30ea9e1a20>",
- "_predict": "<function ActorCriticPolicy._predict at 0x7d30ea9e1ab0>",
- "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d30ea9e1b40>",
- "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d30ea9e1bd0>",
- "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d30ea9e1c60>",
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7a91a50455a0>",
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a91a5045630>",
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a91a50456c0>",
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a91a5045750>",
+ "_build": "<function ActorCriticPolicy._build at 0x7a91a50457e0>",
+ "forward": "<function ActorCriticPolicy.forward at 0x7a91a5045870>",
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a91a5045900>",
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a91a5045990>",
+ "_predict": "<function ActorCriticPolicy._predict at 0x7a91a5045a20>",
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a91a5045ab0>",
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a91a5045b40>",
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a91a5045bd0>",
  "__abstractmethods__": "frozenset()",
- "_abc_impl": "<_abc._abc_data object at 0x7d30eab8dc80>"
+ "_abc_impl": "<_abc._abc_data object at 0x7a91a5048800>"
  },
  "verbose": 1,
  "policy_kwargs": {},
- "num_timesteps": 1015808,
- "_total_timesteps": 1000000,
+ "num_timesteps": 1512000,
+ "_total_timesteps": 1500000,
  "_num_timesteps_at_start": 0,
  "seed": null,
  "action_noise": null,
- "start_time": 1701857765345600882,
+ "start_time": 1702395982267112737,
  "learning_rate": 0.0003,
  "tensorboard_log": null,
  "_last_obs": {
  ":type:": "<class 'numpy.ndarray'>",
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  },
  "_last_episode_starts": {
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@@ -41,17 +41,17 @@
  "_episode_num": 0,
  "use_sde": false,
  "sde_sample_freq": -1,
- "_current_progress_remaining": -0.015808000000000044,
+ "_current_progress_remaining": -0.008000000000000007,
  "_stats_window_size": 100,
  "ep_info_buffer": {
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