Risk-Sensitive Policy with Distributional Reinforcement Learning

TL;DR

This paper introduces a risk-sensitive reinforcement learning approach using distributional RL to incorporate risk considerations into decision policies, balancing expected return and risk in a practical, interpretable manner.

Contribution

It proposes a novel risk-based utility function derived from the return distribution, enabling risk-sensitive policies with minimal modifications to existing distributional RL algorithms.

Findings

Effective risk-sensitive policies learned with distributional RL

Balances risk minimization and return maximization

Provides interpretable decision-making framework

Abstract

Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the $Q$ function generally standing at the core of learning schemes in RL by another function taking into account both the expected return and the risk. Named the risk-based utility function $U$, it can be extracted from the random return distribution $Z$ naturally learnt by any distributional RL algorithm. This enables to span the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, and with an emphasis on the interpretability of the resulting decision-making process.