An Empirical Study of Deep Reinforcement Learning in Continuing Tasks

Yi Wan, Dmytro Korenkevych, Zheqing Zhu

Keywords: reinforcement learning, continuing tasks, empirical study

TL;DR: We provide an empirical study of several well-known deep RL algorithms using a suite of continuing task testbeds, highlighting several key insights concerning continuing tasks.

Abstract: In reinforcement learning (RL), continuing tasks refer to tasks where the agent-environment interaction is ongoing and can not be broken down into episodes. These tasks are suitable when environment resets are unavailable, agent-controlled, or predefined but where all rewards—including those beyond resets—are critical. These scenarios frequently occur in real-world applications and can not be modeled by episodic tasks. While modern deep RL algorithms have been extensively studied and well understood in episodic tasks, their behavior in continuing tasks remains underexplored. To address this gap, we provide an empirical study of several well-known deep RL algorithms using a suite of continuing task testbeds based on Mujoco and Atari environments, highlighting several key insights concerning continuing tasks. Using these testbeds, we also investigate the effectiveness of a method for improving temporal-difference-based reinforcement learning (RL) algorithms in continuing tasks by centering rewards, as introduced by \citet{naik2024reward}. While their work primarily focused on this method in conjunction with Q-learning, our results extend their findings by demonstrating that this method is effective across a broader range of algorithms, scales to larger tasks, and outperforms two other reward-centering approaches.

Primary Area: reinforcement learning

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Submission Number: 13038