Towards better dense rewards in Reinforcement Learning Applications

TL;DR

This paper reviews challenges and recent approaches in designing effective dense reward functions for reinforcement learning, aiming to improve learning efficiency and alignment with task objectives in complex environments.

Contribution

It analyzes current methods for constructing dense rewards and proposes new strategies to enhance their effectiveness and reliability in diverse RL applications.

Findings

Dense rewards improve exploration and learning speed.

Poorly designed rewards can cause unintended behaviors.

Recent methods like inverse RL and reward modeling show promise.

Abstract

Finding meaningful and accurate dense rewards is a fundamental task in the field of reinforcement learning (RL) that enables agents to explore environments more efficiently. In traditional RL settings, agents learn optimal policies through interactions with an environment guided by reward signals. However, when these signals are sparse, delayed, or poorly aligned with the intended task objectives, agents often struggle to learn effectively. Dense reward functions, which provide informative feedback at every step or state transition, offer a potential solution by shaping agent behavior and accelerating learning. Despite their benefits, poorly crafted reward functions can lead to unintended behaviors, reward hacking, or inefficient exploration. This problem is particularly acute in complex or high-dimensional environments where handcrafted rewards are difficult to specify and validate. To address this, recent research has explored a variety of approaches, including inverse reinforcement learning, reward modeling from human preferences, and self-supervised learning of intrinsic rewards. While these methods offer promising directions, they often involve trade-offs between generality, scalability, and alignment with human intent. This proposal explores several approaches to dealing with these unsolved problems and enhancing the effectiveness and reliability of dense reward construction in different RL applications.