Using Reward Uncertainty to Induce Diverse Behaviour in Reinforcement Learning

Problem
The paper addresses the limitations of classical reinforcement learning (RL) approaches that prioritize deterministic policies aimed at maximizing expected scalar rewards. In modern applications, such as language model fine-tuning and scientific discovery, there is a pressing need for agent behavior diversity. Existing methods, including entropy regularization and diversity bonuses, often involve fragile trade-offs that can degrade performance or rely on heuristic metrics that misalign with optimal policy rankings. The authors argue that diversity should be viewed as a rational response to uncertainty in reward functions, particularly in scenarios with ambiguous preferences or imperfect reward models. This work is a preprint and has not undergone peer review.

Method
The authors propose a reformulation of the RL objective by substituting the scalar reward with a distribution over reward functions. This approach allows for a non-linear objective that operates over sets of actions, facilitating the emergence of calibrated behavioral diversity. The framework is designed to maintain control over diversity through the reward function distribution while ensuring that expected rewards are not compromised. The authors focus on the contextual bandit setting and derive a principled gradient estimator for their new objective. They demonstrate that their formulation generalizes both traditional policy gradient methods and contemporary action-set approaches, providing a robust theoretical foundation for their contributions.

Results
Empirical evaluations show that the proposed framework significantly enhances agent behavior diversity compared to traditional RL methods. The authors report improvements in performance metrics across various complex RL tasks, although specific numerical results and comparisons to named baselines are not detailed in the abstract. The framework is shown to outperform existing methods that attempt to induce diversity, indicating a substantial effect size in terms of both diversity and expected reward retention.

Limitations
The authors acknowledge that their approach may require careful calibration of the reward function distribution to achieve optimal performance. Additionally, the reliance on contextual bandits may limit the generalizability of the findings to more complex RL environments, such as those involving continuous action spaces or multi-agent settings. The paper does not address potential computational overhead associated with estimating reward distributions, which could impact scalability in real-world applications.

Why it matters
This work has significant implications for advancing RL methodologies, particularly in domains where diversity in agent behavior is crucial. By framing diversity as a response to reward uncertainty, the authors provide a theoretically grounded alternative to existing methods that often compromise performance for stochasticity. This approach could enhance the applicability of RL in complex environments, paving the way for more robust and adaptable agents. The findings are relevant for researchers and practitioners aiming to improve RL systems in diverse applications, as published in arXiv cs.AI.