Aggregation of Reinforcement Learning Algorithms

Reinforcement learning (RL) is a machine learning method that can learn an optimal strategy for a system without knowing the mathematical model of the system. Many RL algorithms are successfully applied in various fields. However, each algorithm has its advantages and disadvantages. With the increasing complexity of environments and tasks, it is difficult for a single learning algorithm to cope with complicated learning problems with high performance. This motivated us to combine some learning algorithms to improve the learning quality. This paper proposes a new multiple learning architecture, "aggregated multiple reinforcement learning system (AMRLS)". AMRLS adopts three different learning algorithms to learn individually and then combines their results with aggregation methods. To evaluate its performance, AMRLS is tested on two different environments: a cart-pole system and a maze environment. The presented simulation results reveal that aggregation not only provides robustness and fault tolerance ability, but also produces more smooth learning curves and needs fewer learning steps than individual learning algorithms.