A policy-improving system with a mixture probability and clustering distributions to unknown 3d-environments

There are many proposed policy-improving system of Reinforcement Learning (RL) agents that effective in quickly adapting to environmental change by using many statistical methods, such as using a Mixture Model of Bayesian network, using Mixture Probability and Clustering Distribution, etc. However, by using a mixture model of Bayesian network, this system increase the computational complexity that make the control of the computational complexity becomes a necessary problem. On the other hand, by using mixture probability and clustering distribution, even though the computational complexity can be controlled and simultaneously maintain the system´s performance, the examination of computational complexity load and the adaptation performance to more complex environments such as 3D-environments are required. In this paper, we concentrate on the policy-improving system by using mixture probability and clustering distributions. We introduce new parameters and the modified reward process for experiments on 3D-environments, and then investigate and discuss the performance of our proposed system from the results.