Reinforcement Learning with Dynamic Convex Risk Measures

• URL: http://arxiv.org/abs/2112.13414v1
• Date: Sun, 26 Dec 2021 16:41:05 GMT
• Title: Reinforcement Learning with Dynamic Convex Risk Measures
• Authors: Anthony Coache and Sebastian Jaimungal
• Abstract summary: We develop an approach for solving time-consistent risk-sensitive optimization problems using model-free reinforcement learning (RL) We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules. We further develop an actor-critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to optimization problems in statistical arbitrage trading and obstacle avoidance robot control.

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