Cooperative and Competitive Reinforcement and Imitation Learning for a Mixture of Heterogeneous Learning Modules.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.  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.
CRAIL
Cooperative and Competitive Reinforcement and Imitation Learning for a Mixture of Heterogeneous Learning Modules.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.  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.
CRAIL