Minimizing expected loss for risk-avoiding reinforcement learning

This paper considers the design of a reinforcement learning (RL) agent that can strike a balance between return and risk. First, we discuss several favorable properties of an RL risk model, and then propose a definition of risk based on expected negative rewards. We also design a Q-decomposition-based framework that allows a reinforcement learning agent to control the balance between risk and profit. The results of experiments on both artificial and real-world stock datasets demonstrate that the proposed risk model satisfies the beneficial properties of an RL-based risk learning model, and also significantly outperforms other approaches in terms of avoiding risks.