Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning

TL;DR

This paper introduces a new distributional reinforcement learning algorithm that optimizes a broad class of static spectral risk measures, providing better risk management and interpretability in decision-making tasks.

Contribution

The paper develops a novel DRL algorithm with convergence guarantees that optimizes spectral risk measures, extending beyond CVaR, and offers a clearer interpretation of learned policies.

Findings

Outperforms existing risk-neutral DRL models in various tasks.

Learns policies aligned with spectral risk measures.

Provides theoretical convergence guarantees.

Abstract

In domains such as finance, healthcare, and robotics, managing worst-case scenarios is critical, as failure to do so can lead to catastrophic outcomes. Distributional Reinforcement Learning (DRL) provides a natural framework to incorporate risk sensitivity into decision-making processes. However, existing approaches face two key limitations: (1) the use of fixed risk measures at each decision step often results in overly conservative policies, and (2) the interpretation and theoretical properties of the learned policies remain unclear. While optimizing a static risk measure addresses these issues, its use in the DRL framework has been limited to the simple static CVaR risk measure. In this paper, we present a novel DRL algorithm with convergence guarantees that optimizes for a broader class of static Spectral Risk Measures (SRM). Additionally, we provide a clear interpretation of the learned policy by leveraging the distribution of returns in DRL and the decomposition of static coherent risk measures. Extensive experiments demonstrate that our model learns policies aligned with the SRM objective, and outperforms existing risk-neutral and risk-sensitive DRL models in various settings.