test
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +96 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/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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- 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: MlpPolicy
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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: LunarLander-v2
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 251.35 +/- 19.22
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name: mean_reward
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verified: false
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---
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# **MlpPolicy** Agent playing **LunarLander-v2**
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This is a trained model of a **MlpPolicy** 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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{"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 0x787cf16de4d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x787cf16de560>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x787cf16de5f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x787cf16de680>", "_build": "<function ActorCriticPolicy._build at 0x787cf16de710>", "forward": "<function ActorCriticPolicy.forward at 0x787cf16de7a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x787cf16de830>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x787cf16de8c0>", "_predict": "<function ActorCriticPolicy._predict at 0x787cf16de950>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x787cf16de9e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x787cf16dea70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x787cf16deb00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x787cf1679fc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, 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":type:": "<class 'function'>",
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}
|
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ppo-LunarLander-v2/policy.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:de6e96ec4f59efd52ce4d890adb204b2f792ce8cb798c2ae0b303b3f4aa3203a
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size 88490
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ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
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|
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|
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version https://git-lfs.github.com/spec/v1
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oid sha256:01b6371005c93643ff9036235242f2347cbb160112cf6402f864e6e409208f8b
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size 43762
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ppo-LunarLander-v2/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
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size 864
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ppo-LunarLander-v2/system_info.txt
ADDED
@@ -0,0 +1,9 @@
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|
|
|
|
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|
1 |
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- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
|
2 |
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- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.0.0a5
|
4 |
+
- PyTorch: 2.2.1+cu121
|
5 |
+
- GPU Enabled: True
|
6 |
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- Numpy: 1.25.2
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.28.1
|
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+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
Binary file (191 kB). View file
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|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 251.35179680000002, "std_reward": 19.215396123988665, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-26T15:59:11.271441"}
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