A Robust Approach for Continuous Interactive Reinforcement Learning

Abstract

Interactive reinforcement learning is an approach in which an external trainer helps an agent to learn through advice. A trainer is useful in large or continuous scenarios; however, when the characteristics of the environment change over time, it can affect the learning. Robust reinforcement learning is a reliable approach that allows an agent to learn a task, regardless of disturbances in the environment. In this work, we present an approach that addresses interactive reinforcement learning problems in a dynamic environment with continuous states and actions. Our results show that the proposed approach allows an agent to complete the cart-pole balancing task satisfactorily in a dynamic, continuous action-state domain.