Analysis of Off-Policy Multi-Step TD-Learning with Linear Function Approximation

arXiv:2402.15781v1 Announce Type: cross
Abstract: This paper analyzes multi-step TD-learning algorithms within the `deadly triad' scenario, characterized by linear function approximation, off-policy learning, and bootstrapping. In particular, we prove that n-step TD-learning algorithms converge to a solution as the sampling horizon n increases sufficiently. The paper is divided into two parts. In the first part, we comprehensively examine the fundamental properties of their model-based deterministic counterparts, including projected value iteration, gradient descent algorithms, and the control theoretic approach, which can …

abstract algorithms analysis approximation arxiv bootstrapping converge cs.lg cs.sy eess.sy function horizon linear paper policy prove sampling solution type