LunarLander MLP model
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2-Liog.zip +3 -0
- ppo-LunarLander-v2-Liog/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2-Liog/data +96 -0
- ppo-LunarLander-v2-Liog/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2-Liog/policy.pth +3 -0
- ppo-LunarLander-v2-Liog/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2-Liog/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: PPO-MLP
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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: 246.46 +/- 32.22
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name: mean_reward
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verified: false
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---
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# **PPO-MLP** Agent playing **LunarLander-v2**
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This is a trained model of a **PPO-MLP** 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 0x7c5663374b80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c5663374c10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c5663374ca0>", 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ppo-LunarLander-v2-Liog/policy.optimizer.pth
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size 88490
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ppo-LunarLander-v2-Liog/policy.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:fbac17a1d19c35bcc605f4d145df0e7b97815626852b901cb5b2eec0bd84fef1
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size 43762
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ppo-LunarLander-v2-Liog/pytorch_variables.pth
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
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ppo-LunarLander-v2-Liog/system_info.txt
ADDED
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1 |
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- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
|
2 |
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- Python: 3.10.12
|
3 |
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- Stable-Baselines3: 2.0.0a5
|
4 |
+
- PyTorch: 2.1.0+cu118
|
5 |
+
- GPU Enabled: True
|
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- Numpy: 1.23.5
|
7 |
+
- Cloudpickle: 2.2.1
|
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+
- Gymnasium: 0.28.1
|
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+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
Binary file (140 kB). View file
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results.json
ADDED
@@ -0,0 +1 @@
|
|
|
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|
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{"mean_reward": 246.4551019, "std_reward": 32.223107933639355, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-03T18:48:29.809568"}
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