Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks

Published: 16 Jan 2024, Last Modified: 06 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Reinforcement Learning; Multitask Learning; Exploration
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TL;DR: We rigorously show that algorithm running myopic exploration with policy-sharing across tasks can be sample-efficient when the task set is diverse.
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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Primary Area: reinforcement learning
Submission Number: 4126