@ARTICLE{10.3389/fnbot.2018.00061, 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/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.} }