LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

2023/10/05 by Kim, Woojun, Kim, Jeonghye, Sung, Youngchul · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2310.03342

Abstract

In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model. The proposed framework learns to integrate a set of diverse exploration strategies so that the agent can adaptively select the most effective exploration strategy over time to realize a relevant exploration-exploitation trade-off for each given task. The effectiveness of the proposed exploration framework is demonstrated by various experiments in the MiniGrid and Atari environments.

Cited by

• D3HRL: A Distributed Hierarchical Reinforcement Learning Approach Based on Causal Discovery and Spurious Correlation Detection

Related

• An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options Framework
• A Survey of Exploration Methods in Reinforcement Learning
• Reinforcement Learning with a Focus on Adjusting Policies to Reach Targets
• Exploration Potential
• Never Give Up: Learning Directed Exploration Strategies