Swarm Inverse Reinforcement Learning for Biological Systems

Complex global behavior can emerge from local interactions in biological systems. Many models have been introduced to describe the interaction rules of biological individuals. Nonetheless, most research efforts cannot capture the inner cognitive and sequential decision process of individual animals in their swarms. In this paper, we formulate this problem as homogeneous Markov game and focus on identifying the potential reward function of individual animals so as to understand their collective behaviors. We propose an inverse reinforcement learning method PS-AIRL specifically for biological systems, where the parameter sharing paradigm is combined with a deep inverse reinforcement learning. Theoretical analysis and experimental evaluation show that PS-AIRL can learn the policy and the reward function from collective behavior demonstrations. Moreover, our methods can be applied to a wide range of biological behavioral studies.