Upload PPO LunarLander-v2 trained agent
Browse files- README.md +15 -8
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/_stable_baselines3_version +1 -1
- ppo-LunarLander-v2/data +38 -38
- ppo-LunarLander-v2/policy.optimizer.pth +2 -2
- ppo-LunarLander-v2/policy.pth +2 -2
- ppo-LunarLander-v2/system_info.txt +3 -3
- replay.mp4 +0 -0
- results.json +1 -1
README.md
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---
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tags:
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- LunarLander-v2
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- ppo
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- deep-reinforcement-learning
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- reinforcement-learning
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- deep-rl-course
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model-index:
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- name: PPO
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results:
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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---
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library_name: stable-baselines3
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tags:
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- LunarLander-v2
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- deep-reinforcement-learning
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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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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 257.11 +/- 25.27
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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)
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TODO: Add your code
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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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```
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config.json
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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 0x7f12f6596c20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f12f6596cb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f12f6596d40>", 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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 0x7f6b8997dea0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6b8997df30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6b8997dfc0>", 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"_shape": [
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"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
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"high": "[inf inf inf inf inf inf inf inf]",
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"bounded_below": "[False False False False False False False False]",
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"bounded_above": "[False False False False False False False False]",
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"n_envs": 16,
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"start_time":
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"lr_schedule": {
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":serialized:": "
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"n_steps": 1024,
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"gamma": 0.999,
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"gae_lambda": 0.98,
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| 4 |
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
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| 5 |
"__module__": "stable_baselines3.common.policies",
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| 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 ",
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| 7 |
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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f6b8997dea0>",
|
| 8 |
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6b8997df30>",
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| 9 |
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6b8997dfc0>",
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| 10 |
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6b8997e050>",
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| 11 |
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"_build": "<function ActorCriticPolicy._build at 0x7f6b8997e0e0>",
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| 12 |
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"forward": "<function ActorCriticPolicy.forward at 0x7f6b8997e170>",
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| 13 |
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f6b8997e200>",
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| 14 |
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6b8997e290>",
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| 15 |
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"_predict": "<function ActorCriticPolicy._predict at 0x7f6b8997e320>",
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| 16 |
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6b8997e3b0>",
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| 17 |
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6b8997e440>",
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| 18 |
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6b8997e4d0>",
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| 19 |
"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7f6b8996f740>"
|
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},
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| 22 |
"verbose": 1,
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| 23 |
"policy_kwargs": {},
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"num_timesteps": 1015808,
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"seed": null,
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"action_noise": null,
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"start_time": 1682073694494488069,
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"learning_rate": 0.0003,
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"tensorboard_log": null,
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"lr_schedule": {
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| 35 |
},
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"_last_obs": {
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":type:": "<class 'numpy.ndarray'>",
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