A reinforcement learning system by using a mixture model of Bayesian network

In this research, we propose a system improving reinforcement learning agents´ policies by using a mixture of Bayesian Networks (BNs) to adapt the agents to dynamic environments. A BN is one of stochastic models and used as agents´ stochastic knowledge. In our system, models corresponding to new environments are represented by the mixture distribution of BNs constructed in advance.