An Actor-Critic Algorithm with Function Approximation for Risk Sensitive Cost Markov Decision Processes
This work addresses a less-studied problem in reinforcement learning for applications requiring risk-sensitive decision-making, representing an incremental advancement.
The paper tackles the risk-sensitive cost criterion for Markov decision processes by developing a model-free actor-critic algorithm with function approximation, and it demonstrates superior performance over other recent algorithms in numerical experiments.
In this paper, we consider the risk-sensitive cost criterion with exponentiated costs for Markov decision processes and develop a model-free policy gradient algorithm in this setting. Unlike additive cost criteria such as average or discounted cost, the risk-sensitive cost criterion is less studied due to the complexity resulting from the multiplicative structure of the resulting Bellman equation. We develop an actor-critic algorithm with function approximation in this setting and provide its asymptotic convergence analysis. We also show the results of numerical experiments that demonstrate the superiority in performance of our algorithm over other recent algorithms in the literature.