Learning Continuous Action Models in a Real-Time Strategy Environment

摘要

Although several researchers have integrated methods for reinforcement learning (RL) with case-based reasoning (CBR) to model continuous action spaces, existing integrations typically employ discrete approximations of these models. This limits the set of actions that can be modeled, and may lead to non-optimal solutions. We introduce the Continuous Action and State Space Learner (CASSL), an integrated RL/CBR algorithm that uses continuous models directly. Our empirical study shows that CASSL significantly outperforms two baseline approaches for selecting actions on a task from a real-time strategy gaming environment.