Updating the PPO agent

README.md

@@ -5,7 +5,6 @@ tags:
 - deep-reinforcement-learning
 - reinforcement-learning
 - stable-baselines3
-- gymnasium
 model-index:
 - name: PPO
   results:
@@ -17,57 +16,22 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 240.31 +/- 69.19
+      value: 264.51 +/- 16.47
       name: mean_reward
       verified: false
-language:
-- en
-pipeline_tag: reinforcement-learning
 ---
 
 # **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) 
-
-This model is trained with the help of [Deep RL Course by HuggingFace](https://huggingface.co/learn/deep-rl-course/unit0/introduction)
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)
-```python
-# necessary libraries
-import gymnasium as gym
-
-from huggingface_sb3 import load_from_hub, package_to_hub
-from huggingface_hub import (
-    notebook_login,
-)
+TODO: Add your code
 
-from stable_baselines3 import PPO
-from stable_baselines3.common.env_util import make_vec_env
-from stable_baselines3.common.evaluation import evaluate_policy
-from stable_baselines3.common.monitor import Monitor
 
-# Step 1 : Create an environment
-env = gym.make("LunarLander-v2")
-observation,info = env.reset() # initialize the environment
-
-# Step 2 : Create the model
-model = PPO(
-    policy = "MlpPolicy", # Multiple Layer Perceptron Policy
-    env = env,
-    n_steps = 1024,
-    batch_size = 64,
-    n_epochs = 5,
-    gamma = 0.995, # discount factor
-    gae_lambda = 0.98, # close to 1 - more bias and less variance
-    ent_coef = 0.01, # exploration exploitation tradeoff
-    verbose = 1
-)
-
-# Step 3 : Train the model
-model.learn(total_timesteps=2000000,progress_bar = True)
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
 
-# Step 4 : Evaluation
-eval_env = Monitor(gym.make("LunarLander-v2"))
-mean_reward,std_reward = evaluate_policy(model,eval_env,n_eval_episodes = 10 ,deterministic=True)
-print(f"Mean reward : {mean_reward} +/- {std_reward}")
-```
+...
+```

config.json

@@ -1 +1 @@
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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 0x7fe0af0a9c60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe0af0a9cf0>", "reset_noise": 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ppo-lunar-lander-v2.zip

@@ -1,3 +1,3 @@
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ppo-lunar-lander-v2/data

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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 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 0x7fe0af0a9c60>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe0af0a9cf0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe0af0a9d80>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe0af0a9e10>",
-        "_build": "<function ActorCriticPolicy._build at 0x7fe0af0a9ea0>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7fe0af0a9f30>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe0af0a9fc0>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe0af0aa050>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7fe0af0aa0e0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe0af0aa170>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe0af0aa200>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe0af0aa290>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x79b9787f1090>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79b9787f1120>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79b9787f11b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79b9787f1240>",
+        "_build": "<function ActorCriticPolicy._build at 0x79b9787f12d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x79b9787f1360>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x79b9787f13f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79b9787f1480>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x79b9787f1510>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79b9787f15a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79b9787f1630>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x79b9787f16c0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fe0af042140>"
+        "_abc_impl": "<_abc._abc_data object at 0x79b9787f46c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
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     "seed": null,
     "action_noise": null,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
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@@ -80,14 +80,14 @@
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     "gamma": 0.995,
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