Initial training run
Browse files- README.md +4 -4
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +24 -24
- ppo-LunarLander-v2/policy.optimizer.pth +1 -1
- ppo-LunarLander-v2/policy.pth +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
README.md
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@@ -6,7 +6,7 @@ tags:
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name:
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results:
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- task:
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type: reinforcement-learning
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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 267.
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name: mean_reward
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verified: false
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---
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# **PPO
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This is a trained model of a **PPO
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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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- 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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type: LunarLander-v2
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metrics:
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- type: mean_reward
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value: 267.03 +/- 18.75
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name: mean_reward
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verified: false
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---
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# **PPO** Agent playing **LunarLander-v2**
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This is a trained model of a **PPO** agent playing **LunarLander-v2**
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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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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 0x7f73df8815e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f73df881670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f73df881700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f73df881790>", "_build": "<function 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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 0x7f7bd352c5e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7bd352c670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7bd352c700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7bd352c790>", "_build": "<function 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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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"batch_size": 128,
|
86 |
"n_epochs": 8,
|
87 |
"clip_range": {
|
88 |
":type:": "<class 'function'>",
|
ppo-LunarLander-v2/policy.optimizer.pth
CHANGED
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|
1 |
version https://git-lfs.github.com/spec/v1
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2 |
-
oid sha256:
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3 |
size 87929
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version https://git-lfs.github.com/spec/v1
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size 87929
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ppo-LunarLander-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
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|
1 |
version https://git-lfs.github.com/spec/v1
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2 |
-
oid sha256:
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3 |
size 43201
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
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+
oid sha256:e71d36e76a36b3b7207aca045f430c1d403d0213d2389d0fe792ad62c5e6b23c
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size 43201
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replay.mp4
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
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|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward": 267.
|
|
|
1 |
+
{"mean_reward": 267.02927881124145, "std_reward": 18.750423681766364, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-10T21:06:26.668658"}
|