commit a2c-PandaReachDense-v2
Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +2 -2
- a2c-PandaReachDense-v2/_stable_baselines3_version +1 -1
- a2c-PandaReachDense-v2/data +37 -36
- a2c-PandaReachDense-v2/policy.optimizer.pth +1 -1
- a2c-PandaReachDense-v2/policy.pth +1 -1
- a2c-PandaReachDense-v2/system_info.txt +2 -2
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +2 -2
README.md
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@@ -16,7 +16,7 @@ model-index:
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type: PandaReachDense-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: PandaReachDense-v2
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metrics:
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- type: mean_reward
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value: -0.76 +/- 0.28
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name: mean_reward
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verified: false
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---
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a2c-PandaReachDense-v2.zip
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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass 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 (Tuple)\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). 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