Upload PPO LunarLander-v2 trained agent

README.md

@@ -1,11 +1,10 @@
 ---
+library_name: stable-baselines3
 tags:
 - LunarLander-v2
-- ppo
 - deep-reinforcement-learning
 - reinforcement-learning
-- custom-implementation
-- deep-rl-course
+- stable-baselines3
 model-index:
 - name: PPO
   results:
@@ -17,45 +16,22 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 161.63 +/- 10.99
+      value: 245.52 +/- 21.29
       name: mean_reward
       verified: false
 ---
 
-  # 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).
 
-  This is a trained model of a PPO agent playing LunarLander-v2.
-    
-  # Hyperparameters
-  ```python
-  {'exp_name': '/home/zz/DOD/DeepRL/HuggingFaceCourse/Proximal Policy Optimization'
-'seed': 1
-'torch_deterministic': True
-'cuda': True
-'track': False
-'wandb_project_name': 'ppoLunarLander'
-'wandb_entity': None
-'capture_video': False
-'env_id': 'LunarLander-v2'
-'total_timesteps': 10000000
-'learning_rate': 2.5e-05
-'num_envs': 8
-'num_steps': 2048
-'anneal_lr': True
-'gae': True
-'gamma': 0.999
-'gae_lambda': 0.98
-'num_minibatches': 512
-'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
-'repo_id': 'zzen0008/ppo-LunarLander-v2'
-'batch_size': 16384
-'minibatch_size': 32}
-  ```
-  
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_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 0x7f89736b5af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f89736b5b80>", "reset_noise": 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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 0x7fab2ad75af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fab2ad75b80>", "reset_noise": 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ppo_lunar_landar/_stable_baselines3_version

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ppo_lunar_landar/data

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         "__module__": "stable_baselines3.common.policies",
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+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7fab2ad75af0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fab2ad75b80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fab2ad75c10>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fab2ad75ca0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fab2ad75d30>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fab2ad75dc0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fab2ad75e50>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fab2ad75ee0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fab2ad75f70>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fab2ad79040>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fab2ad790d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fab2ad79160>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f89736b65c0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7fab2ad76900>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -48,7 +48,7 @@
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     "seed": null,
     "action_noise": null,
-    "start_time": 1678250480051653327,
+    "start_time": 1678651856726034995,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,7 +57,7 @@
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     "_last_episode_starts": {
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@@ -70,7 +70,7 @@
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     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -83,7 +83,7 @@
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 64,
+    "batch_size": 128,
     "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",

ppo_lunar_landar/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1244dcdfcde298d41f17a1939b2f4085fadb3aaceef03233497415a339c29a1e
+oid sha256:a964bf5aa5a7930b8479ae095a13451d1d30b13e8c1301873cb2c8dd94c84e15
 size 87929

ppo_lunar_landar/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8fb379940f3509e588806cc33f707bdf700582dc072eb05e8b72cb4076354cc8
-size 43393
+oid sha256:5720e67f6c0425762c98923e16f9188b91a93e61b539b17ae45e2258e4166644
+size 43329

ppo_lunar_landar/system_info.txt

@@ -1,6 +1,6 @@
 - OS: Linux-5.10.0-20-amd64-x86_64-with-glibc2.31 # 1 SMP Debian 5.10.158-2 (2022-12-13)
 - Python: 3.9.14
-- Stable-Baselines3: 1.7.0
+- Stable-Baselines3: 1.8.0a2
 - PyTorch: 1.13.1+cu117
 - GPU Enabled: True
 - Numpy: 1.24.2

results.json

@@ -1 +1 @@
-{"env_id": "LunarLander-v2", "mean_reward": 161.63281851538468, "std_reward": 10.993666667692338, "n_evaluation_episodes": 10, "eval_datetime": "2023-03-13T02:13:31.919003"}
+{"mean_reward": 245.5205252221114, "std_reward": 21.29322567221237, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-13T07:32:47.598040"}