Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes

• URL: http://arxiv.org/abs/2407.03065v1
• Date: Wed, 3 Jul 2024 12:36:24 GMT
• Title: Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
• Authors: Asaf Cassel, Aviv Rosenberg,
• Abstract summary: Policy Optimization (PO) methods are among the most popular Reinforcement Learning (RL) algorithms in practice. This paper proposes a PO-based algorithm with rate-optimal regret guarantees under the linear Markov Decision Process (MDP) model. Our algorithm achieves regret with improved dependence on the other parameters of the problem.
• Score: 12.76843681997386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Policy Optimization (PO) methods are among the most popular Reinforcement Learning (RL) algorithms in practice. Recently, Sherman et al. [2023a] proposed a PO-based algorithm with rate-optimal regret guarantees under the linear Markov Decision Process (MDP) model. However, their algorithm relies on a costly pure exploration warm-up phase that is hard to implement in practice. This paper eliminates this undesired warm-up phase, replacing it with a simple and efficient contraction mechanism. Our PO algorithm achieves rate-optimal regret with improved dependence on the other parameters of the problem (horizon and function approximation dimension) in two fundamental settings: adversarial losses with full-information feedback and stochastic losses with bandit feedback.

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