Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning

Simone Parisi*, Davide Tateo, Maximilian Hensel, Carlo D’eramo, Jan Peters, Joni Pajarinen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by learning a separate function assessing the exploration value of the actions. Contrary to existing methods that use models of reward and dynamics, our approach is off-policy and model-free. We further propose new tabular environments for benchmarking exploration in reinforcement learning. Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function. Results also suggest that our approach scales gracefully with the size of the environment.

Original languageEnglish
Article number81
Pages (from-to)1-44
Number of pages44
JournalALGORITHMS
Volume15
Issue number3
DOIs
Publication statusPublished - 28 Feb 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Exploration
  • Off-policy
  • Reinforcement learning
  • Sparse reward
  • Upper confidence bound

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