2018/02/19 by Matthew Fellows, Fellows, Matthew, Kamil Ciosek +3 · 1 voice
Computer Science · Engineering · #Adaptive Dynamic Programming Control #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Fuel Cells and Related Materials #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1802.06891
openalex publication_date 2018/02/19 · arxiv published 2018/02/19 · arxiv created 2018/05/30 · arxiv updated 2018/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new way of deriving policy gradient updates for reinforcement learning. Our technique, based on Fourier analysis, recasts integrals that arise with expected policy gradients as convolutions and turns them into multiplications. The obtained analytical solutions allow us to capture the low variance benefits of EPG in a broad range of settings. For the critic, we treat trigonometric and radial basis functions, two function families with the universal approximation property. The choice of policy can be almost arbitrary, including mixtures or hybrid continuous-discrete probability distributions. Moreover, we derive a general family of sample-based estimators for stochastic policy gradients, which unifies existing results on sample-based approximation. We believe that this technique has the potential to shape the next generation of policy gradient approaches, powered by analytical results.