State Chrono Representation for Enhancing Generalization in Reinforcement Learning

• URL: http://arxiv.org/abs/2411.06174v1
• Date: Sat, 09 Nov 2024 13:12:34 GMT
• Title: State Chrono Representation for Enhancing Generalization in Reinforcement Learning
• Authors: Jianda Chen, Wen Zheng Terence Ng, Zichen Chen, Sinno Jialin Pan, Tianwei Zhang,
• Abstract summary: In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. We propose a novel State Chrono Representation (SCR) approach to address these challenges. SCR augments state metric-based representations by incorporating extensive temporal information into the update step of bisimulation metric learning.
• Score: 36.12688166503104
• License:
• Abstract: In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. Recent advancements in metric learning, such as deep bisimulation metric approaches, have shown promising results in learning structured low-dimensional representation space from pixel observations, where the distance between states is measured based on task-relevant features. However, these approaches face challenges in demanding generalization tasks and scenarios with non-informative rewards. This is because they fail to capture sufficient long-term information in the learned representations. To address these challenges, we propose a novel State Chrono Representation (SCR) approach. SCR augments state metric-based representations by incorporating extensive temporal information into the update step of bisimulation metric learning. It learns state distances within a temporal framework that considers both future dynamics and cumulative rewards over current and long-term future states. Our learning strategy effectively incorporates future behavioral information into the representation space without introducing a significant number of additional parameters for modeling dynamics. Extensive experiments conducted in DeepMind Control and Meta-World environments demonstrate that SCR achieves better performance comparing to other recent metric-based methods in demanding generalization tasks. The codes of SCR are available in https://github.com/jianda-chen/SCR.

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