second attempt

README.md

@@ -6,7 +6,7 @@ tags:
 - reinforcement-learning
 - stable-baselines3
 model-index:
-- name: PPO
+- name: ppo
   results:
   - task:
       type: reinforcement-learning
@@ -16,119 +16,22 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 227.87 +/- 85.21
+      value: 253.80 +/- 22.65
       name: mean_reward
       verified: false
 ---
 
-# **PPO** Agent playing **LunarLander-v2**
-This is a trained model of a **PPO** agent playing **LunarLander-v2**
+# **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).
 
 ## Usage (with Stable-baselines3)
-
+TODO: Add your code
 
 
 ```python
-# Virtual display
-from pyvirtualdisplay import Display
-
-virtual_display = Display(visible=0, size=(1400, 900))
-virtual_display.start()
-
-# We create our environment with gym.make("<name_of_the_environment>")
-env = gym.make("LunarLander-v2")
-env.reset()
-print("_____OBSERVATION SPACE_____ \n")
-print("Observation Space Shape", env.observation_space.shape)
-print("Sample observation", env.observation_space.sample()) # Get a random observation
-
-print("\n _____ACTION SPACE_____ \n")
-print("Action Space Shape", env.action_space.n)
-print("Action Space Sample", env.action_space.sample()) # Take a random action
-
-# Create the environment
-env = make_vec_env('LunarLander-v2', n_envs=16)
-
-# TODO: Define a PPO MlpPolicy architecture
-# We use MultiLayerPerceptron (MLPPolicy) because the input is a vector,
-# if we had frames as input we would use CnnPolicy
-# SOLUTION
-# We added some parameters to accelerate the training
-model = PPO(
-    policy = 'MlpPolicy',
-    env = env,
-    n_steps = 1024,
-    batch_size = 64,
-    n_epochs = 4,
-    gamma = 0.999,
-    gae_lambda = 0.98,
-    ent_coef = 0.01,
-    verbose=1)
-
-# Train it for 1,000,000 timesteps
-model.learn(total_timesteps=1000000)
-# Save the model
-model_name = "ppo-LunarLander-v2"
-model.save(model_name)
-
-
-# Create a new environment for evaluation
-eval_env = gym.make("LunarLander-v2")
-
-# Evaluate the model with 10 evaluation episodes and deterministic=True
-mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
-
-# Print the results
-print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
 
 ...
 ```
-
-Then upload it using huggingFace library
-
-notebook_login()
-
-!git config --global credential.helper store
-
-and
-
-```python
-import gym
-from stable_baselines3.common.vec_env import DummyVecEnv
-from stable_baselines3.common.env_util import make_vec_env
-
-from huggingface_sb3 import package_to_hub
-
-## TODO: Define a repo_id
-## repo_id is the id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
-repo_id = "rlucasz93/ppo-LunarLander-v2"
-
-# TODO: Define the name of the environment
-env_id = "LunarLander-v2"
-
-# Create the evaluation env
-eval_env = DummyVecEnv([lambda: gym.make(env_id)])
-
-
-# TODO: Define the model architecture we used
-model_architecture = "PPO"
-
-## TODO: Define the commit message
-commit_message = "first model upload"
-
-# method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
-package_to_hub(model=model, # Our trained model
-               model_name=model_name, # The name of our trained model 
-               model_architecture=model_architecture, # The model architecture we used: in our case PPO
-               env_id=env_id, # Name of the environment
-               eval_env=eval_env, # Evaluation Environment
-               repo_id=repo_id, # id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
-               commit_message=commit_message)
-
-# Note: if after running the package_to_hub function and it gives an issue of rebasing, please run the following code
-# cd <path_to_repo> && git add . && git commit -m "Add message" && git pull 
-# And don't forget to do a "git push" at the end to push the change to the hub.
-...
-```
-

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 0x7fe7eeb6e1f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe7eeb6e280>", "reset_noise": 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ppo-LunarLander-v2/_stable_baselines3_version

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         "__module__": "stable_baselines3.common.policies",
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@@ -68,15 +42,41 @@
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     "sde_sample_freq": -1,
     "_current_progress_remaining": -0.015808000000000044,
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     "ep_info_buffer": {
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     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
     "_n_updates": 248,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
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ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
-- Python: 3.8.10
-- Stable-Baselines3: 1.7.0
-- PyTorch: 1.13.1+cu116
+- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.0+cu118
 - GPU Enabled: True
-- Numpy: 1.21.6
-- Gym: 0.21.0
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+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

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