Upload PPO Pendulum-v1 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-pendulum-v1.zip +3 -0
- ppo-pendulum-v1/_stable_baselines3_version +1 -0
- ppo-pendulum-v1/data +105 -0
- ppo-pendulum-v1/policy.optimizer.pth +3 -0
- ppo-pendulum-v1/policy.pth +3 -0
- ppo-pendulum-v1/pytorch_variables.pth +3 -0
- ppo-pendulum-v1/system_info.txt +9 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
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---
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library_name: stable-baselines3
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tags:
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- Pendulum-v1
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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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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: Pendulum-v1
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type: Pendulum-v1
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metrics:
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- type: mean_reward
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value: -1348.57 +/- 247.55
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name: mean_reward
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verified: false
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---
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# **PPO** Agent playing **Pendulum-v1**
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This is a trained model of a **PPO** agent playing **Pendulum-v1**
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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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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7ee850864e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ee850864ee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ee850864f70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ee850865000>", "_build": "<function ActorCriticPolicy._build at 0x7ee850865090>", "forward": "<function ActorCriticPolicy.forward at 0x7ee850865120>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ee8508651b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ee850865240>", "_predict": "<function ActorCriticPolicy._predict at 0x7ee8508652d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ee850865360>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ee8508653f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ee850865480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ee85085cd80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2048, "_total_timesteps": 1000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1693366798510593647, "learning_rate": 0.001, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAALSSTL/y5hk/AO2yPpSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsDhpSMAUOUdJRSlC4="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": true, "sde_sample_freq": 4, "_current_progress_remaining": -1.048, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": 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ppo-pendulum-v1.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:1611ee4c62105db46dc92747dac5c0407dc78cce41007761fa679449a0e73727
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size 133788
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ppo-pendulum-v1/_stable_baselines3_version
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2.0.0a5
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ppo-pendulum-v1/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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"__module__": "stable_baselines3.common.policies",
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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 0x7ee850864e50>",
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"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ee850864ee0>",
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"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ee850864f70>",
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"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ee850865000>",
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"_build": "<function ActorCriticPolicy._build at 0x7ee850865090>",
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"forward": "<function ActorCriticPolicy.forward at 0x7ee850865120>",
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"extract_features": "<function ActorCriticPolicy.extract_features at 0x7ee8508651b0>",
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"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ee850865240>",
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"_predict": "<function ActorCriticPolicy._predict at 0x7ee8508652d0>",
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"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ee850865360>",
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"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ee8508653f0>",
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"predict_values": "<function ActorCriticPolicy.predict_values at 0x7ee850865480>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7ee85085cd80>"
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},
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"verbose": 1,
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"policy_kwargs": {},
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"num_timesteps": 2048,
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"_total_timesteps": 1000,
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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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},
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"_last_episode_starts": {
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":type:": "<class 'numpy.ndarray'>",
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