Learning State-Specific Action Masks for Reinforcement Learning

Author:

Wang Ziyi12,Li Xinran12,Sun Luoyang12,Zhang Haifeng123,Liu Hualin4,Wang Jun5

Affiliation:

1. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China

3. Nanjing Artificial Intelligence Research of IA, Jiangning District, Nanjing 211135, China

4. Key Laboratory of Oil & Gas Business Chain Optimization, Petrochina Planning and Engineering Institute, CNPC, Beijing 100083, China

5. Computer Science, University College London, London WC1E 6BT, UK

Abstract

Efficient yet sufficient exploration remains a critical challenge in reinforcement learning (RL), especially for Markov Decision Processes (MDPs) with vast action spaces. Previous approaches have commonly involved projecting the original action space into a latent space or employing environmental action masks to reduce the action possibilities. Nevertheless, these methods often lack interpretability or rely on expert knowledge. In this study, we introduce a novel method for automatically reducing the action space in environments with discrete action spaces while preserving interpretability. The proposed approach learns state-specific masks with a dual purpose: (1) eliminating actions with minimal influence on the MDP and (2) aggregating actions with identical behavioral consequences within the MDP. Specifically, we introduce a novel concept called Bisimulation Metrics on Actions by States (BMAS) to quantify the behavioral consequences of actions within the MDP and design a dedicated mask model to ensure their binary nature. Crucially, we present a practical learning procedure for training the mask model, leveraging transition data collected by any RL policy. Our method is designed to be plug-and-play and adaptable to all RL policies, and to validate its effectiveness, an integration into two prominent RL algorithms, DQN and PPO, is performed. Experimental results obtained from Maze, Atari, and μRTS2 reveal a substantial acceleration in the RL learning process and noteworthy performance improvements facilitated by the introduced approach.

Publisher

MDPI AG

Subject

Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science

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