TASAC: Temporally Abstract Soft Actor-Critic for Continuous Control

• URL: http://arxiv.org/abs/2104.06521v1
• Date: Tue, 13 Apr 2021 21:24:44 GMT
• Title: TASAC: Temporally Abstract Soft Actor-Critic for Continuous Control
• Authors: Haonan Yu, Wei Xu, Haichao Zhang
• Abstract summary: TASAC is an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic framework. It has two benefits compared to traditional off-policy RL algorithms: persistent exploration and an unbiased multi-step Q operator for TD learning.
• Score: 28.534585378574143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose temporally abstract soft actor-critic (TASAC), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner. TASAC adds a second-stage binary policy to choose between the previous action and the action output by an SAC actor. It has two benefits compared to traditional off-policy RL algorithms: persistent exploration and an unbiased multi-step Q operator for TD learning. We demonstrate its advantages over several strong baselines across 5 different categories of 14 continuous control tasks, in terms of both sample efficiency and final performance. Because of its simplicity and generality, TASAC can serve as a drop-in replacement for SAC when temporal abstraction is needed.

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