Switched policy network architecture
Browse files- a2c-PandaPickAndPlace-v3.zip +2 -2
- a2c-PandaPickAndPlace-v3/data +56 -54
- a2c-PandaPickAndPlace-v3/policy.optimizer.pth +2 -2
- a2c-PandaPickAndPlace-v3/policy.pth +2 -2
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +2 -2
a2c-PandaPickAndPlace-v3.zip
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size
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a2c-PandaPickAndPlace-v3/data
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"__doc__": "\n Dict Rollout buffer used in on-policy algorithms like A2C/PPO.\n Extends the RolloutBuffer to use dictionary observations\n\n It corresponds to ``buffer_size`` transitions collected\n using the current policy.\n This experience will be discarded after the policy update.\n In order to use PPO objective, we also store the current value of each state\n and the log probability of each taken action.\n\n The term rollout here refers to the model-free notion and should not\n be used with the concept of rollout used in model-based RL or planning.\n Hence, it is only involved in policy and value function training but not action selection.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n Equivalent to Monte-Carlo advantage estimate when set to 1.\n :param gamma: Discount factor\n :param n_envs: Number of parallel environments\n ",
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It allows to keep variance\n above zero and prevent it from growing too fast. 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