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Neighboring state-based RL Exploration

2022-12-21 01:23:53
Jeffery Cheng, Kevin Li, Justin Lin, Pedro Pachuca

Abstract

Reinforcement Learning is a powerful tool to model decision-making processes. However, it relies on an exploration-exploitation trade-off that remains an open challenge for many tasks. In this work, we study neighboring state-based, model-free exploration led by the intuition that, for an early-stage agent, considering actions derived from a bounded region of nearby states may lead to better actions when exploring. We propose two algorithms that choose exploratory actions based on a survey of nearby states, and find that one of our methods, ${\rho}$-explore, consistently outperforms the Double DQN baseline in an discrete environment by 49\% in terms of Eval Reward Return.

Abstract (translated)

URL

https://arxiv.org/abs/2212.10712

PDF

https://arxiv.org/pdf/2212.10712.pdf


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