Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation

• URL: http://arxiv.org/abs/2106.07472v1
• Date: Mon, 14 Jun 2021 14:59:05 GMT
• Title: Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation
• Authors: Anas Barakat, Pascal Bianchi, Julien Lehmann
• Abstract summary: Actor-critic methods integrating target networks have exhibited a stupendous empirical success in deep reinforcement learning. We bridge this gap by proposing the first theoretical analysis of an online target-based actor-critic with linear function approximation in the discounted reward setting.
• Score: 2.1592777170316366
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Actor-critic methods integrating target networks have exhibited a stupendous empirical success in deep reinforcement learning. However, a theoretical understanding of the use of target networks in actor-critic methods is largely missing in the literature. In this paper, we bridge this gap between theory and practice by proposing the first theoretical analysis of an online target-based actor-critic algorithm with linear function approximation in the discounted reward setting. Our algorithm uses three different timescales: one for the actor and two for the critic. Instead of using the standard single timescale temporal difference (TD) learning algorithm as a critic, we use a two timescales target-based version of TD learning closely inspired from practical actor-critic algorithms implementing target networks. First, we establish asymptotic convergence results for both the critic and the actor under Markovian sampling. Then, we provide a finite-time analysis showing the impact of incorporating a target network into actor-critic methods.

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