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ppo-test-unit1-RLcourse

Browse files
README.md CHANGED
@@ -6,7 +6,7 @@ tags:
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  - reinforcement-learning
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  - stable-baselines3
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  model-index:
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- - name: PPO, MLP, 1000000 epochs
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  results:
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  - task:
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  type: reinforcement-learning
@@ -16,13 +16,13 @@ model-index:
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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- value: 277.59 +/- 19.53
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  name: mean_reward
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  verified: false
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  ---
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- # **PPO, MLP, 1000000 epochs** Agent playing **LunarLander-v2**
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- This is a trained model of a **PPO, MLP, 1000000 epochs** agent playing **LunarLander-v2**
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  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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  ## Usage (with Stable-baselines3)
 
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  - reinforcement-learning
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  - stable-baselines3
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  model-index:
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+ - name: PPO
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  results:
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  - task:
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  type: reinforcement-learning
 
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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+ value: 262.59 +/- 13.90
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  name: mean_reward
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  verified: false
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  ---
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+ # **PPO** Agent playing **LunarLander-v2**
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+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
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  using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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  ## Usage (with Stable-baselines3)
config.json CHANGED
@@ -1 +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 0x7fe415ecb370>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe415ecb400>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe415ecb490>", 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  "__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 ",
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- "__init__": "<function ActorCriticPolicy.__init__ at 0x7fe415ecb370>",
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- "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe415ecb400>",
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- "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe415ecb490>",
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- "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe415ecb520>",
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- "_build": "<function ActorCriticPolicy._build at 0x7fe415ecb5b0>",
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- "forward": "<function ActorCriticPolicy.forward at 0x7fe415ecb640>",
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- "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe415ecb6d0>",
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- "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe415ecb760>",
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- "_predict": "<function ActorCriticPolicy._predict at 0x7fe415ecb7f0>",
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- "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe415ecb880>",
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- "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe415ecb910>",
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- "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe415ecb9a0>",
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  "__abstractmethods__": "frozenset()",
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- "_abc_impl": "<_abc._abc_data object at 0x7fe3ce5a1fc0>"
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  },
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- "verbose": 1,
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  "policy_kwargs": {},
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  "num_timesteps": 1015808,
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  "_total_timesteps": 1000000,
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  "_num_timesteps_at_start": 0,
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  "seed": null,
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  "action_noise": null,
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- "start_time": 1684967043076598723,
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  "learning_rate": 0.0003,
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  "tensorboard_log": null,
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  "_last_obs": {
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  ":type:": "<class 'numpy.ndarray'>",
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  },
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  "_last_episode_starts": {
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  ":type:": "<class 'numpy.ndarray'>",
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  },
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  "_last_original_obs": null,
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  "_episode_num": 0,
@@ -45,16 +45,16 @@
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  "_stats_window_size": 100,
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  "ep_info_buffer": {
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  },
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  "ep_success_buffer": {
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  },
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- "_n_updates": 744,
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  "observation_space": {
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  "dtype": "float32",
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  "bounded_below": "[ True True True True True True True True]",
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  "bounded_above": "[ True True True True True True True True]",
@@ -69,7 +69,7 @@
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  },
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  "action_space": {
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  ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
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  "n": "4",
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  "start": "0",
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  "_shape": [],
@@ -87,13 +87,13 @@
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  "n_epochs": 4,
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  "clip_range": {
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  ":type:": "<class 'function'>",
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  },
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  "clip_range_vf": null,
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  "normalize_advantage": true,
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  "target_kl": null,
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  "lr_schedule": {
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  ":type:": "<class 'function'>",
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98
  }
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  }
 
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  "__module__": "stable_baselines3.common.policies",
6
  "__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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7d915539b9c0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d915539ba60>",
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+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d915539bb00>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d915539bba0>",
11
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