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arXiv · 2010.11364

Sample Efficient Reinforcement Learning with REINFORCE

Junzi Zhang·Jongho Kim·Brendan O'Donoghue·Stephen Boyd
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Abstract

Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visitation measure based mini-batch stochastic gradients with a diverging batch size, which limit their applicability in practical scenarios. In this paper, we consider classical policy gradient methods that compute an approximate gradient with a single trajectory or a fixed size mini-batch of trajectories under soft-max parametrization and log-barrier regularization, along with the widely-used REINFORCE gradient estimation procedure. By controlling the number of "bad" episodes and resorting to the classical doubling trick, we establish an anytime sub-linear high probability regret bound as well as almost sure global convergence of the average regret with an asymptotically sub-linear rate. These provide the first set of global convergence and sample efficiency results for the well-known REINFORCE algorithm and contribute to a better understanding of its performance in practice.

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Junzi Zhang, Jongho Kim, Brendan O'Donoghue, Stephen Boyd. 2020-12-24. Sample Efficient Reinforcement Learning with REINFORCE. https://arxiv.org/abs/2010.11364

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