A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning

Tongzheng Ren, Tianjun Zhang, Csaba Szepesvari, Bo Dai

Keywords: representation learning, reinforcement learning

Abstract: Representation learning lies at the heart of the empirical success of deep learning for dealing with the curse of dimensionality. However, the power of representation learning has not been fully exploited yet in reinforcement learning (RL), due to i), the trade-off between expressiveness and tractability; and ii), the coupling between exploration and representation learning. In this paper, we first reveal the fact that under some noise assumption in the stochastic control model, we can obtain the linear spectral feature of its corresponding Markov transition operator in closed-form for free. Based on this observation, we propose Spectral Dynamics Embedding (SPEDE), which breaks the trade-off and completes optimistic exploration for representation learning by exploiting the structure of the noise. We provide rigorous theoretical analysis of SPEDE, and demonstrate the practical superior performance over the existing state-of-the-art empirical algorithms on several benchmarks.

One-sentence Summary: We utilize the structure of noise to propose a provable and practical exploration algorithm for representation learning in reinforcement learning, which has superior performance over the existing state-of-the-art algorithms on several benchmarks.