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Relative Entropy Regularized Sample-Efficient Reinforcement Learning With Continuous Actions

Authors :
Shang, Zhiwei
Li, Renxing
Zheng, Chunhua
Li, Huiyun
Cui, Yunduan
Shang, Zhiwei
Li, Renxing
Zheng, Chunhua
Li, Huiyun
Cui, Yunduan
Publication Year :
2023

Abstract

In this article, a novel reinforcement learning (RL) approach, continuous dynamic policy programming (CDPP), is proposed to tackle the issues of both learning stability and sample efficiency in the current RL methods with continuous actions. The proposed method naturally extends the relative entropy regularization from the value function-based framework to the actor-critic (AC) framework of deep deterministic policy gradient (DDPG) to stabilize the learning process in continuous action space. It tackles the intractable softmax operation over continuous actions in the critic by Monte Carlo estimation and explores the practical advantages of the Mellowmax operator. A Boltzmann sampling policy is proposed to guide the exploration of actor following the relative entropy regularized critic for superior learning capability, exploration efficiency, and robustness. Evaluated by several benchmark and real-robot-based simulation tasks, the proposed method illustrates the positive impact of the relative entropy regularization including efficient exploration behavior and stable policy update in RL with continuous action space and successfully outperforms the related baseline approaches in both sample efficiency and learning stability.

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1422562935
Document Type :
Electronic Resource