Aggregation of tiling-based reinforcement learning algorithms

Reinforcement learning (RL) is a learning technique that learns an optimal policy in case of knowing almost nothing about the dynamics of the environment under consideration. When RL is combined with function approximation schemes, the learning performance is greatly influenced by RL algorithms and learning parameters. This paper proposes a new on-line multiple learning and aggregating architecture, "aggregated multiple reinforcement learning system (AMRLS)". Instead of searching for the optimal learning parameters or featurization schemes, AMRLS attempts to aggregate the outcomes of different learners to produce a better policy. This architecture is tested on the mountain car problem with the aggregation of several related tiling and learning parameters. Experimental results show that AMRLS can improve the learning performance over the use of a single RL algorithm.