mysecond-lunarlander
Browse files- README.md +1 -1
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +20 -20
- 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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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: 282.73 +/- 18.93
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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 0x7d275b679bd0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d275b679c60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d275b679cf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d275b679d80>", "_build": "<function ActorCriticPolicy._build at 0x7d275b679e10>", "forward": "<function ActorCriticPolicy.forward at 0x7d275b679ea0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d275b679f30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d275b679fc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d275b67a050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d275b67a0e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d275b67a170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d275b67a200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d275b8196c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, 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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:bb2c2c84f3809b05dcc0635cb9266ac061314157128b1909d5543b6866b3030f
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| 3 |
size 43762
|
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":
|
|
|
|
| 1 |
+
{"mean_reward": 282.72665470000004, "std_reward": 18.934504657917387, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-15T04:43:55.226202"}
|