kennethgoodman
commited on
Commit
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Parent(s):
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Upload PPO FrozenLake-v1 trained agent
Browse files- FrozenLake-v1-version_0_0_2.zip +3 -0
- FrozenLake-v1-version_0_0_2/_stable_baselines3_version +1 -0
- FrozenLake-v1-version_0_0_2/data +89 -0
- FrozenLake-v1-version_0_0_2/policy.optimizer.pth +3 -0
- FrozenLake-v1-version_0_0_2/policy.pth +3 -0
- FrozenLake-v1-version_0_0_2/pytorch_variables.pth +3 -0
- FrozenLake-v1-version_0_0_2/system_info.txt +7 -0
- README.md +37 -0
- config.json +1 -0
- results.json +1 -0
FrozenLake-v1-version_0_0_2.zip
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version https://git-lfs.github.com/spec/v1
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size 156700
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FrozenLake-v1-version_0_0_2/_stable_baselines3_version
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1.6.2
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FrozenLake-v1-version_0_0_2/data
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{
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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 0x7f2d632a41f0>",
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},
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}
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FrozenLake-v1-version_0_0_2/policy.optimizer.pth
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size 96057
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FrozenLake-v1-version_0_0_2/policy.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:e533cce85064fc42b3458d0cf1544b1d598dc3230d898cf33947305027960eb7
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size 47297
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FrozenLake-v1-version_0_0_2/pytorch_variables.pth
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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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FrozenLake-v1-version_0_0_2/system_info.txt
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OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
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Python: 3.8.15
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Stable-Baselines3: 1.6.2
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PyTorch: 1.12.1+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
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---
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library_name: stable-baselines3
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tags:
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- FrozenLake-v1
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: PPO
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results:
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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: FrozenLake-v1
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type: FrozenLake-v1
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metrics:
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- type: mean_reward
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value: 0.90 +/- 0.30
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name: mean_reward
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verified: false
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---
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# **PPO** Agent playing **FrozenLake-v1**
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This is a trained model of a **PPO** agent playing **FrozenLake-v1**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
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## Usage (with Stable-baselines3)
|
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TODO: Add your code
|
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+
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+
|
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```python
|
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from stable_baselines3 import ...
|
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+
from huggingface_sb3 import load_from_hub
|
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+
|
36 |
+
...
|
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+
```
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config.json
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@@ -0,0 +1 @@
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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 0x7f2d632a41f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2d632a4280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2d632a4310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2d632a43a0>", "_build": "<function 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"normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 0.9, "std_reward": 0.30000000000000004, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-07T16:06:26.304604"}
|