Bibliographic Information: Lepel, O., & Barakat, A. (2024). Beyond Expected Returns: A Policy Gradient Algorithm for Cumulative Prospect Theoretic Reinforcement Learning. arXiv preprint arXiv:2410.02605v1.
Research Objective: This paper aims to develop a policy gradient algorithm for reinforcement learning that optimizes for Cumulative Prospect Theory (CPT) value, addressing the limitations of expected utility theory in capturing human decision-making behavior.
Methodology: The authors derive a novel policy gradient theorem for CPT-based reinforcement learning, generalizing the standard policy gradient theorem. This theorem enables the design of a model-free policy gradient algorithm (CPT-PG) that utilizes quantile estimation to approximate a challenging integral term in the gradient computation.
Key Findings: The paper provides theoretical insights into the nature of optimal policies in CPT-based reinforcement learning, demonstrating that they are generally stochastic and non-Markovian, unlike in standard MDPs. The authors also characterize a family of utility functions (affine and exponential) for which the CPT value objective can be maximized with a Markovian policy when probability distortion is absent. Experiments in traffic control, grid world, and electricity management settings demonstrate the effectiveness of the proposed CPT-PG algorithm, highlighting its ability to learn policies aligned with different risk preferences and its superior scalability to larger state spaces compared to existing zeroth-order algorithms.
Main Conclusions: This work presents a novel policy gradient algorithm for CPT-based reinforcement learning, offering a more realistic approach to modeling human decision-making in sequential decision-making problems. The theoretical analysis and empirical results demonstrate the algorithm's effectiveness and potential for applications where aligning with human risk preferences is crucial.
Significance: This research contributes significantly to the field of reinforcement learning by incorporating CPT, a well-established model of human decision-making, into the optimization framework. This has implications for developing more human-like and aligned AI agents, particularly in domains involving human-in-the-loop scenarios.
Limitations and Future Research: The paper primarily focuses on finite-horizon discounted MDPs. Exploring extensions to infinite-horizon settings and incorporating learning of utility and distortion functions from human feedback are promising directions for future research. Additionally, investigating the application of CPT-based reinforcement learning in multi-agent systems and complex real-world scenarios could yield valuable insights.
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by Olivier Lepe... at arxiv.org 10-04-2024
https://arxiv.org/pdf/2410.02605.pdfDeeper Inquiries