Adapting the Function Approximation Architecture in Online Reinforcement Learning

The performance of a reinforcement learning (RL) system depends on the compu- tational architecture used to approximate a value function. We propose an online RL algorithm for adapting a value function’s architecture and efficiently finding useful nonlinear features. The algorithm is evaluated in a spatial domain with high-dimensional, stochastic observations. Our method outperforms non-adaptive baseline architectures and approaches the performance of an architecture given side- channel information about observational structure. These results are a step towards scalable RL algorithms for more general problem settings, where observational structure is unavailable.