Learning to optimize investment ratios and risk-aware behavior in Compound Deep Reinforcement Learning

Abstract

We propose a method to optimize the investment ratio and learn behaviors considering the risk in Compound Deep Reinforcement Learning. Compound reinforcement learning is reinforcement learning that aims to learn behaviors that maximize the compound returns with the betting fraction parameter. We can maximize the compound return by optimizing the betting fraction. Previous work on compound deep reinforcement learning uses the given betting fractions in the range of zero to one, and it does not consider the investment risk. We propose a method to optimize the betting fraction by adding a network to compound deep Q-network. We also propose a method to learn behavior that reduces the variance of returns.