Upload PPO LunarLander-v2 trained agent 2
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +23 -23
- ppo-LunarLander-v2/policy.optimizer.pth +2 -2
- ppo-LunarLander-v2/policy.pth +2 -2
- ppo-LunarLander-v2/pytorch_variables.pth +2 -2
- ppo-LunarLander-v2/system_info.txt +4 -4
- 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: 262.84 +/- 23.70
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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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{"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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7b108e92d480>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b108e92d510>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b108e92d5a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b108e92d630>", "_build": "<function ActorCriticPolicy._build at 0x7b108e92d6c0>", "forward": "<function ActorCriticPolicy.forward at 0x7b108e92d750>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b108e92d7e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b108e92d870>", "_predict": "<function ActorCriticPolicy._predict at 0x7b108e92d900>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b108e92d990>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b108e92da20>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b108e92dab0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b108e922540>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, 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It allows to keep variance\n above zero and prevent it from growing too fast. 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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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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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| 1 |
-
- OS: Linux-
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| 2 |
-
- Python: 3.10.
|
| 3 |
- Stable-Baselines3: 2.0.0a5
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| 4 |
-
- PyTorch: 2.
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| 5 |
- GPU Enabled: True
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| 6 |
-
- Numpy: 1.
|
| 7 |
- Cloudpickle: 2.2.1
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| 8 |
- Gymnasium: 0.28.1
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| 9 |
- OpenAI Gym: 0.25.2
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| 1 |
+
- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
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| 2 |
+
- Python: 3.10.12
|
| 3 |
- Stable-Baselines3: 2.0.0a5
|
| 4 |
+
- PyTorch: 2.1.0+cu121
|
| 5 |
- GPU Enabled: True
|
| 6 |
+
- Numpy: 1.25.2
|
| 7 |
- Cloudpickle: 2.2.1
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| 8 |
- Gymnasium: 0.28.1
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| 9 |
- OpenAI Gym: 0.25.2
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replay.mp4
CHANGED
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Binary files a/replay.mp4 and b/replay.mp4 differ
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results.json
CHANGED
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@@ -1 +1 @@
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| 1 |
-
{"mean_reward":
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| 1 |
+
{"mean_reward": 262.8413931, "std_reward": 23.70456165410175, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-01T14:35:20.035649"}
|