Uploading PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 279.85 +/- 19.69
+      value: 295.51 +/- 20.94
       name: mean_reward
       verified: false
 ---
@@ -30,34 +30,8 @@ TODO: Add your code
 
 
 ```python
-import gym
-
-from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub
-from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
-
-from stable_baselines3 import PPO
-from stable_baselines3.common.evaluation import evaluate_policy
-from stable_baselines3.common.env_util import make_vec_env
-
-# Create the environment
-env = make_vec_env('LunarLander-v2', n_envs=16)
-
-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 2,000,000 timesteps
-model.learn(total_timesteps=2000000)
-# Save the model
-model_name = "ppo-LunarLander-v2"
-model.save(model_name)
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
 
 ...
 ```

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 0x7fcef0716d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcef0716dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcef0716e50>", "_build_mlp_extractor": "<function 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ppo-LunarLander-v2.zip

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+oid sha256:d0c7095a2fb6bbc1798e9cd2ab75848c3d483c50904c5adbc7c544c0581ca218
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ppo-LunarLander-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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. 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    ",
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-        "forward": "<function ActorCriticPolicy.forward at 0x7fcef069d040>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8893524e50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8893524ee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8893524f70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f889352a040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f889352a0d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f889352a160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f889352a1f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f889352a280>",
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     "verbose": 1,
     "policy_kwargs": {},
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     "seed": null,
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     "tensorboard_log": null,
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     "sde_sample_freq": -1,
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