To the Max: Reinventing Reward in Reinforcement Learning
<|reference_start|>To the Max: Reinventing Reward in Reinforcement Learning: In reinforcement learning (RL), different reward functions can define the same optimal policy but result in drastically different learning performance. For some, the agent gets stuck with a suboptimal behavior, and for others, it solves the task efficiently. Choosing a good reward function is hence an extremely important yet challenging problem. In this paper, we explore an alternative approach for using rewards for learning. We introduce \textit{max-reward RL}, where an agent optimizes the maximum rather than the cumulative reward. Unlike earlier works, our approach works for deterministic and stochastic environments and can be easily combined with state-of-the-art RL algorithms. In the experiments, we study the performance of max-reward RL algorithms in two goal-reaching environments from Gymnasium-Robotics and demonstrate its benefits over standard RL. The code is available at https://github.com/veviurko/To-the-Max.<|reference_end|>
@article{veviurko2024to,
    title={To the Max: Reinventing Reward in Reinforcement Learning},
    author={Grigorii Veviurko, Wendelin B"ohmer, Mathijs de Weerdt},
    journal={arXiv preprint arXiv:2402.01361},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01361},
    primaryClass={cs.LG}
}