Poster

On the Expressivity of Neural Networks for Deep Reinforcement Learning

Kefan Dong · Yuping Luo · Tianhe (Kevin) Yu · Chelsea Finn · Tengyu Ma

Keywords: [ Reinforcement Learning Theory ] [ Reinforcement Learning - Theory ]

[ Abstract ] [ Join Zoom
Please do not share or post zoom links

Abstract:

We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, Q-functions, and dynamics. We show, theoretically and empirically, that even for one-dimensional continuous state space, there are many MDPs whose optimal Q-functions and policies are much more complex than the dynamics. For these MDPs, model-based planning is a favorable algorithm, because the resulting policies can approximate the optimal policy significantly better than a neural network parameterization can, and model-free or model-based policy optimization rely on policy parameterization. Motivated by the theory, we apply a simple multi-step model-based bootstrapping planner (BOOTS) to bootstrap a weak Q-function into a stronger policy. Empirical results show that applying BOOTS on top of model-based or model-free policy optimization algorithms at the test time improves the performance on benchmark tasks.

Chat is not available.