Learning a Rendezvous Task with Dynamic Joint Action Perception

Groups of reinforcement learning agents interacting in a common environment often fail to learn optimal behaviors. Poor performance is particularly common in environments where agents must coordinate with each other to receive rewards and where failed coordination attempts are penalized. This paper studies the effectiveness of the dynamic joint action perception (DJAP) algorithm on a grid-world rendezvous task with this characteristic. The effects of learning rate, exploration strategy, and training time on algorithm effectiveness are discussed. An analysis of the types of tasks for which DJAP learning is appropriate is also presented.