Commit
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Parent(s):
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First Trained PPO lunar agent
Browse files- .gitattributes +1 -0
- Lunar-Landing-PPO.zip +3 -0
- Lunar-Landing-PPO/_stable_baselines3_version +1 -0
- Lunar-Landing-PPO/data +94 -0
- Lunar-Landing-PPO/policy.optimizer.pth +3 -0
- Lunar-Landing-PPO/policy.pth +3 -0
- Lunar-Landing-PPO/pytorch_variables.pth +3 -0
- Lunar-Landing-PPO/system_info.txt +7 -0
- README.md +19 -1
- config.json +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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Lunar-Landing-PPO.zip
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version https://git-lfs.github.com/spec/v1
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size 144102
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Lunar-Landing-PPO/_stable_baselines3_version
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1.5.0
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Lunar-Landing-PPO/data
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"policy_class": {
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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 0x7f8736b5a680>",
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},
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Lunar-Landing-PPO/policy.optimizer.pth
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Lunar-Landing-PPO/policy.pth
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Lunar-Landing-PPO/system_info.txt
ADDED
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OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
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Python: 3.7.13
|
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Stable-Baselines3: 1.5.0
|
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PyTorch: 1.11.0+cu113
|
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+
GPU Enabled: True
|
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Numpy: 1.21.6
|
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+
Gym: 0.21.0
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README.md
CHANGED
@@ -1,3 +1,21 @@
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---
|
2 |
-
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---
|
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|
1 |
---
|
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library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- LunarLander-v2
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
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- stable-baselines3
|
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model-index:
|
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- name: PPO
|
10 |
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results:
|
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- metrics:
|
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- type: mean_reward
|
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value: 184.10 +/- 104.07
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name: mean_reward
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task:
|
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type: reinforcement-learning
|
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name: reinforcement-learning
|
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+
dataset:
|
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+
name: LunarLander-v2
|
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type: LunarLander-v2
|
21 |
---
|
config.json
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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f8736b5a680>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8736b5a710>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8736b5a7a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8736b5a830>", "_build": "<function 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