Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures

Abstract

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 riskaverse setting can reduce the variance and enhance robustness of the results.

Research the Multidimensional Logistic Function in the Intrusion Detection System With Machine Learning

“…Different researchers use different approaches to arrive at a general solution. In particular, in [2] a model for checking the class hierarchy based on classifications is presented, in [3] the solution of the problem is obtained with an unlimited parameterized value of softmax. The research [4] proposes a new method of adversarial learning based on the detection and removal of large values of «weight» coefficients, rather than their algorithmic reduction.…”