Initial commit

README.md

@@ -1,44 +1,36 @@
-
 ---
+library_name: stable-baselines3
 tags:
-- CartPole-v1
-- reinforce
+- AntBulletEnv-v0
+- deep-reinforcement-learning
 - reinforcement-learning
-- custom-implementation
-- deep-rl-class
+- stable-baselines3
 model-index:
-- name: DeepRL
+- name: A2C
   results:
-  - task:
+  - metrics:
+    - type: mean_reward
+      value: 674.86 +/- 130.89
+      name: mean_reward
+    task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
-      name: CartPole-v1
-      type: CartPole-v1
-    metrics:
-    - type: mean_reward
-      value: 500.00 +/- 0.00
-      name: mean_reward
-      verified: false
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
 ---
-    
-  # **ppo** Agent playing **PushBlock**
-  This is a trained model of a **ppo** agent playing **PushBlock** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
-  
-  ## Usage (with ML-Agents)
-  The Documentation: https://github.com/huggingface/ml-agents#get-started
-  We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
 
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
 
-  ### Resume the training
-  ```
-  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
-  ```
-  ### Watch your Agent play
-  You can watch your agent **playing directly in your browser:**.
-  
-  1. Go to https://huggingface.co/spaces/unity/ML-Agents-PushBlock
-  2. Step 1: Write your model_id: jackoyoungblood/DeepRL
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
-  
+...
+```

a2c-AntBulletEnv-v0.zip

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+oid sha256:a07bd36f9dda3ff5bf6bab2351f69de3bdfdc4c63090511e3ca811f587a1c602
+size 129258

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.0

a2c-AntBulletEnv-v0/data

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+{
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+        "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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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+Python: 3.7.13
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