ppo_lunar_1500k_exp01

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 273.03 +/- 17.98
+      value: 275.46 +/- 17.96
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -24,6 +24,7 @@ model-index:
 
 
 
+
   # **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).
   

config.json

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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 0x0000026528F1A700>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x0000026528F1A790>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x0000026528F1A820>", "_build_mlp_extractor": "<function 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ppo-LunarLander-v2.zip

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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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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -78,11 +78,11 @@
     "_n_updates": 460,
     "n_steps": 1024,
     "gamma": 0.999,
-    "gae_lambda": 0.98,
+    "gae_lambda": 0.99,
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 64,
+    "batch_size": 128,
     "n_epochs": 5,
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results.json

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-{"mean_reward": 273.03485435027153, "std_reward": 17.983607577861253, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T09:18:28.122374"}
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