Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures
This addresses the need for reliable performance in risk-averse settings for applications like finance or robotics, though it is incremental as it adapts existing risk measures to RL.
The paper tackles the problem of controlling risk in reinforcement learning by maximizing dynamic risk measures in infinite-horizon Markov Decision Processes, showing that a risk-averse deep Q-learning framework reduces variance and enhances robustness in numerical studies.
Traditional reinforcement learning (RL) aims to maximize the expected total reward, while the risk of uncertain outcomes needs to be controlled to ensure reliable performance in a risk-averse setting. In this paper, we consider the problem of maximizing dynamic risk of a sequence of rewards in infinite-horizon Markov Decision Processes (MDPs). We adapt the Expected Conditional Risk Measures (ECRMs) to the infinite-horizon risk-averse MDP and prove its time consistency. Using a convex combination of expectation and conditional value-at-risk (CVaR) as a special one-step conditional risk measure, we reformulate the risk-averse MDP as a risk-neutral counterpart with augmented action space and manipulation on the immediate rewards. We further prove that the related Bellman operator is a contraction mapping, which guarantees the convergence of any value-based RL algorithms. Accordingly, we develop a risk-averse deep Q-learning framework, and our numerical studies based on two simple MDPs show that the risk-averse setting can reduce the variance and enhance robustness of the results.