Upload baseline PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 269.76 +/- 17.60
+      value: 247.79 +/- 12.29
       name: mean_reward
       verified: false
 ---
@@ -26,20 +26,12 @@ 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
-from stable_baselines3 import PPO
-from stable_baselines3.common.env_util import make_vec_env
+from stable_baselines3 import ...
 from huggingface_sb3 import load_from_hub
 
-checkpoint = load_from_hub(
-  "quilaquedi/ppo-LunarLander-v2",
-  "LunarLander-v2"
-)
-
-model = PPO.load(checkpoint)
-
-env = make_vec_env("LunarLander-v2", n_envs=16)
-
+...
 ```

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 0x7f7b482cbc10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7b482cbca0>", "reset_noise": 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lunarlander-v2_ppo_v0/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 0x7f7b482cbc10>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7b482cbca0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7b482cbd30>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7b482cbdc0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f7b482cbe50>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f7b482cbee0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7b482cbf70>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7b482cd040>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f7b482cd0d0>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7b482cd160>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7b482cd1f0>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7b482cd280>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f28da1d9c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f28da1d9ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f28da1d9d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f28da1d9dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f28da1d9e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f28da1d9ee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f28da1d9f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f28da1df040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f28da1df0d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f28da1df160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f28da1df1f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f28da1df280>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f7b482ce040>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f28da1dbcc0>"
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     "verbose": 1,
     "policy_kwargs": {},
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+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
         "_shape": [
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         ],
-        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
-        "high": "[inf inf inf inf inf inf inf inf]",
-        "bounded_below": "[False False False False False False False False]",
-        "bounded_above": "[False False False False False False False False]",
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+        "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.       ]",
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+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
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+        "n": "4",
+        "start": "0",
         "_shape": [],
         "dtype": "int64",
         "_np_random": null
@@ -48,12 +51,12 @@
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1678867849269562758,
+    "start_time": 1678887866702921130,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
         ":type:": "<class 'function'>",
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     "_last_episode_starts": {
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+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -84,7 +87,7 @@
     "n_epochs": 4,
     "clip_range": {
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lunarlander-v2_ppo_v0/system_info.txt

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 - Python: 3.9.16
-- Stable-Baselines3: 1.7.0
+- Stable-Baselines3: 2.0.0a0
 - PyTorch: 1.13.1+cu116
 - GPU Enabled: True
 - Numpy: 1.22.4
-- Gym: 0.21.0
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results.json

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