Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks

• URL: http://arxiv.org/abs/2403.01636v2
• Date: Wed, 6 Mar 2024 04:34:01 GMT
• Title: Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks
• Authors: Ziping Xu, Zifan Xu, Runxuan Jiang, Peter Stone, Ambuj Tewari
• Abstract summary: This paper shows that when an agent is trained on a sufficiently diverse set of tasks, a generic policy-sharing algorithm with myopic exploration design can be sample-efficient. To the best of our knowledge, this is the first theoretical demonstration of the "exploration benefits" of MTRL.
• Score: 53.44714413181162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multitask Reinforcement Learning (MTRL) approaches have gained increasing attention for its wide applications in many important Reinforcement Learning (RL) tasks. However, while recent advancements in MTRL theory have focused on the improved statistical efficiency by assuming a shared structure across tasks, exploration--a crucial aspect of RL--has been largely overlooked. This paper addresses this gap by showing that when an agent is trained on a sufficiently diverse set of tasks, a generic policy-sharing algorithm with myopic exploration design like $\epsilon$-greedy that are inefficient in general can be sample-efficient for MTRL. To the best of our knowledge, this is the first theoretical demonstration of the "exploration benefits" of MTRL. It may also shed light on the enigmatic success of the wide applications of myopic exploration in practice. To validate the role of diversity, we conduct experiments on synthetic robotic control environments, where the diverse task set aligns with the task selection by automatic curriculum learning, which is empirically shown to improve sample-efficiency.

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