AUTHOR=Uchibe Eiji TITLE=Cooperative and Competitive Reinforcement and Imitation Learning for a Mixture of Heterogeneous Learning Modules JOURNAL=Frontiers in Neurorobotics VOLUME=12 YEAR=2018 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2018.00061 DOI=10.3389/fnbot.2018.00061 ISSN=1662-5218 ABSTRACT=

This paper proposes Cooperative and competitive Reinforcement And Imitation Learning (CRAIL) for selecting an appropriate policy from a set of multiple heterogeneous modules and training all of them in parallel. Each learning module has its own network architecture and improves the policy based on an off-policy reinforcement learning algorithm and behavior cloning from samples collected by a behavior policy that is constructed by a combination of all the policies. Since the mixing weights are determined by the performance of the module, a better policy is automatically selected based on the learning progress. Experimental results on a benchmark control task show that CRAIL successfully achieves fast learning by allowing modules with complicated network structures to exploit task-relevant samples for training.