Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2407.10775v1
• Date: Mon, 15 Jul 2024 14:54:57 GMT
• Title: Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
• Authors: Alessandro Montenegro, Marco Mussi, Matteo Papini, Alberto Maria Metelli,
• Abstract summary: We introduce an exploration-agnostic algorithm, called C-PG, which exhibits global last-ite convergence guarantees under (weak) gradient domination assumptions. We numerically validate our algorithms on constrained control problems, and compare them with state-of-the-art baselines.
• Score: 62.81324245896717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Constrained Reinforcement Learning (CRL) tackles sequential decision-making problems where agents are required to achieve goals by maximizing the expected return while meeting domain-specific constraints, which are often formulated as expected costs. In this setting, policy-based methods are widely used since they come with several advantages when dealing with continuous-control problems. These methods search in the policy space with an action-based or parameter-based exploration strategy, depending on whether they learn directly the parameters of a stochastic policy or those of a stochastic hyperpolicy. In this paper, we propose a general framework for addressing CRL problems via gradient-based primal-dual algorithms, relying on an alternate ascent/descent scheme with dual-variable regularization. We introduce an exploration-agnostic algorithm, called C-PG, which exhibits global last-iterate convergence guarantees under (weak) gradient domination assumptions, improving and generalizing existing results. Then, we design C-PGAE and C-PGPE, the action-based and the parameter-based versions of C-PG, respectively, and we illustrate how they naturally extend to constraints defined in terms of risk measures over the costs, as it is often requested in safety-critical scenarios. Finally, we numerically validate our algorithms on constrained control problems, and compare them with state-of-the-art baselines, demonstrating their effectiveness.

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