Upload PPO LunarLander-v2 trained agent
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2-5.zip +3 -0
- ppo-LunarLander-v2-5/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2-5/data +94 -0
- ppo-LunarLander-v2-5/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2-5/policy.pth +3 -0
- ppo-LunarLander-v2-5/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2-5/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -1
README.md
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value:
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name: mean_reward
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verified: false
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---
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 195.20 +/- 75.69
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name: mean_reward
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verified: false
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---
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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 0x7f15b30085e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f15b3008670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f15b3008700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f15b3008790>", "_build": "<function 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":type:": "<class 'abc.ABCMeta'>",
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"__module__": "stable_baselines3.common.policies",
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"__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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"__init__": "<function ActorCriticPolicy.__init__ at 0x7f19bed3c820>",
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},
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"clip_range_vf": null,
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"normalize_advantage": true,
|
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+
"target_kl": null
|
94 |
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}
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ppo-LunarLander-v2-5/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
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|
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|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:fd305f8715c85a1d3549f02c20d5be0fbab350c15a1ba5b1abc2596180610200
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3 |
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size 87929
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ppo-LunarLander-v2-5/policy.pth
ADDED
@@ -0,0 +1,3 @@
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|
|
|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:39b555cc1b4b3483d3dcaaac2a1a2542eb6b199a25a76c864f24414b81f5f5c6
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3 |
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size 43201
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ppo-LunarLander-v2-5/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
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|
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|
|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
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size 431
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ppo-LunarLander-v2-5/system_info.txt
ADDED
@@ -0,0 +1,7 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
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OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
2 |
+
Python: 3.8.16
|
3 |
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Stable-Baselines3: 1.6.2
|
4 |
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PyTorch: 1.13.0+cu116
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
replay.mp4
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 195.19671480123992, "std_reward": 75.68964916102392, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T17:55:44.251439"}
|