torkable commited on
Commit
b32b7b6
1 Parent(s): b333665

balance that shit

Browse files
CartPole-v1.zip ADDED
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+ {
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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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CartPole-v1/system_info.txt ADDED
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+ - OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
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+ - Python: 3.10.12
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+ - Stable-Baselines3: 2.0.0a5
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+ - PyTorch: 2.0.1+cu118
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+ - GPU Enabled: True
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+ - Numpy: 1.23.5
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+ - Cloudpickle: 2.2.1
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+ - Gymnasium: 0.28.1
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+ - OpenAI Gym: 0.25.2
README.md ADDED
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1
+ ---
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+ library_name: stable-baselines3
3
+ tags:
4
+ - CartPole-v1
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
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+ - name: PPO
10
+ results:
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+ - task:
12
+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: CartPole-v1
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+ type: CartPole-v1
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+ metrics:
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+ - type: mean_reward
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+ value: 500.00 +/- 0.00
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+ name: mean_reward
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+ verified: false
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+ ---
23
+
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+ # **PPO** Agent playing **CartPole-v1**
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+ This is a trained model of a **PPO** agent playing **CartPole-v1**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+
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+ ## Usage (with Stable-baselines3)
29
+ TODO: Add your code
30
+
31
+
32
+ ```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 ADDED
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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 0x7e786a64a710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e786a64a7a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e786a64a830>", 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replay.mp4 ADDED
Binary file (49 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 500.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-17T23:47:02.243747"}