ppo_lunar_09_back_to_best

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -271.00 +/- 100.99
+      value: 268.36 +/- 12.39
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -27,6 +27,7 @@ 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 0x000002172C9FB700>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000002172C9FB790>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000002172C9FB820>", "_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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     "n_steps": 1024,
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     "gae_lambda": 0.98,
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
-    "batch_size": 64,
+    "batch_size": 128,
     "n_epochs": 5,
     "clip_range": {
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ppo-LunarLander-v2/policy.optimizer.pth

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-size 84829
+oid sha256:1be2c52a7772977b497dbd8e18d1e1d91abefcc399542338dc800f5b198c84c0
+size 84893

ppo-LunarLander-v2/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:197d0a380733d9459359b413d0fef54266cb53c8ce620410f1a4ff5a52871495
+oid sha256:5da4308ab6aeddacf286e13035a23a0bc1e79d94a733d9699e58135612908e5e
 size 43201

ppo-LunarLander-v2/system_info.txt

@@ -1,4 +1,4 @@
-OS: Windows-10-10.0.22610-SP0 10.0.22610
+OS: Windows-10-10.0.22616-SP0 10.0.22616
 Python: 3.8.10
 Stable-Baselines3: 1.5.0
 PyTorch: 1.11.0+cu113

results.json

@@ -1 +1 @@
-{"mean_reward": -270.99993921439165, "std_reward": 100.98857459626826, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T20:21:30.893725"}
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