Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2024

Abstract

Value-based reinforcement learning is the current State-Of-The-Art due to high sampling efficiency. However, our study shows it suffers from low exploitation in early training period and bias sensitiveness. To address these issues, we propose to augment the decision-making process with hypothesis, a weak form of environment description. Our approach relies on prompting the learning agent with accurate hypotheses, and designing a ready-to-adapt policy through incremental learning. We propose the ALH algorithm, showing detailed analyses on a typical learning scheme and a diverse set of Mujoco benchmarks. Our algorithm produces a significant improvement over value-based learning algorithms and other strong baselines. Our code is available at Github URL.

Keywords

Reinforcement learning, Augmented decision-making, Machine learning agent, Incremental learning

Discipline

Artificial Intelligence and Robotics | Computer Sciences

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of the 41st International Conference on Machine Learning (ICML 2024) : Vienna, Austria, July 21-27

Volume

235

First Page

41804

Last Page

41820

Publisher

PMLR

City or Country

Austria

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