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README.md

@@ -1,10 +1,11 @@
 ---
-library_name: stable-baselines3
 tags:
 - LunarLander-v2
+- ppo
 - deep-reinforcement-learning
 - reinforcement-learning
-- stable-baselines3
+- custom-implementation
+- deep-rl-course
 model-index:
 - name: PPO
   results:
@@ -16,22 +17,45 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 296.52 +/- 12.94
+      value: -136.79 +/- 101.06
       name: mean_reward
       verified: false
 ---
 
-# **PPO** Agent playing **LunarLander-v2**
-This is a trained model of a **PPO** agent playing **LunarLander-v2**
-using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+  # PPO Agent Playing LunarLander-v2
 
-## Usage (with Stable-baselines3)
-TODO: Add your code
-
-
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
-
-...
-```
+  This is a trained model of a PPO agent playing LunarLander-v2.
+    
+  # Hyperparameters
+  ```python
+  {'repo_id': 'ThePianist/ppo-LunarLander-v2'
+'exp_name': 'ppo'
+'seed': 1
+'torch_deterministic': True
+'cuda': True
+'track': False
+'wandb_project_name': 'cleanRL'
+'wandb_entity': None
+'capture_video': False
+'env_id': 'LunarLander-v2'
+'total_timesteps': 50000
+'learning_rate': 0.00025
+'num_envs': 4
+'num_steps': 128
+'anneal_lr': True
+'gae': True
+'gamma': 0.99
+'gae_lambda': 0.95
+'num_minibatches': 4
+'update_epochs': 4
+'norm_adv': True
+'clip_coef': 0.2
+'clip_vloss': True
+'ent_coef': 0.01
+'vf_coef': 0.5
+'max_grad_norm': 0.5
+'target_kl': None
+'batch_size': 512
+'minibatch_size': 128}
+  ```
+  

config.json

@@ -1 +1 @@
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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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f2f19453040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2f194530d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2f19453160>", "_build_mlp_extractor": "<function 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"
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     "ep_success_buffer": {
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-    "_n_updates": 612,
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