Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation
This addresses the problem of efficient reinforcement learning for practitioners by providing theoretical guarantees for neural TD algorithms, though it is incremental as it builds on existing TD learning frameworks.
The paper tackled the convergence of temporal difference learning with neural network approximation, proving that max-norm regularization improves state-of-the-art sample complexity and overparameterization bounds.
In this paper, we study the dynamics of temporal difference learning with neural network-based value function approximation over a general state space, namely, \emph{Neural TD learning}. We consider two practically used algorithms, projection-free and max-norm regularized Neural TD learning, and establish the first convergence bounds for these algorithms. An interesting observation from our results is that max-norm regularization can dramatically improve the performance of TD learning algorithms, both in terms of sample complexity and overparameterization. In particular, we prove that max-norm regularization improves state-of-the-art sample complexity and overparameterization bounds. The results in this work rely on a novel Lyapunov drift analysis of the network parameters as a stopped and controlled random process.