Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning

TL;DR

This paper introduces a novel approach to reinforcement learning that leverages representation learning in both state and action spaces to improve efficiency and generalization in continuous action environments, demonstrated through multiple experiments.

Contribution

It extends state representation techniques to action spaces and proposes a two-scale learning framework combining large unsupervised models with small RL policy models.

Findings

Improved policy learning efficiency in continuous action spaces.

Enhanced generalization to large-scale action environments.

Validated effectiveness on MountainCar, CarRacing, and Cheetah tasks.

Abstract

Deep reinforcement learning (DRL) breaks through the bottlenecks of traditional reinforcement learning (RL) with the help of the perception capability of deep learning and has been widely applied in real-world problems.While model-free RL, as a class of efficient DRL methods, performs the learning of state representations simultaneously with policy learning in an end-to-end manner when facing large-scale continuous state and action spaces. However, training such a large policy model requires a large number of trajectory samples and training time. On the other hand, the learned policy often fails to generalize to large-scale action spaces, especially for the continuous action spaces. To address this issue, in this paper we propose an efficient policy learning method in latent state and action spaces. More specifically, we extend the idea of state representations to action representations for better policy generalization capability. Meanwhile, we divide the whole learning task into learning with the large-scale representation models in an unsupervised manner and learning with the small-scale policy model in the RL manner.The small policy model facilitates policy learning, while not sacrificing generalization and expressiveness via the large representation model. Finally,the effectiveness of the proposed method is demonstrated by MountainCar,CarRacing and Cheetah experiments.