Momentum-Based Policy Gradient Methods

Jul 12, 2020

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Policy gradient methods are a class of powerful algorithms in reinforcement learning (RL). More recently, some variance reduced policy gradient methods have been developed to improve sample efficiency and obtain a near-optimal sample complexity O(ϵ^-3) for finding an ϵ-stationary point of non-concave performance function in model-free RL. However, the practical performances of these variance reduced policy gradient methods are not consistent with their near-optimal sample complexity, because these methods require large batches and strict learning rates to achieve this optimal complexity. In the paper, thus, we propose a class of efficient momentum-based policy gradient methods, which use adaptive learning rates and do not require large batches. Specifically, we propose a fast important-sampling momentum-based policy gradient (IS-MBPG) method by using the important sampling technique. Meanwhile, we also propose a fast hessian-aided momentum-based policy gradient (HA-MBPG) method via using the semi-hessian information. In theoretical analysis, we prove that our algorithms also have the sample complexity O(ϵ^-3), as the existing best policy gradient methods. In the experiments, we use some benchmark tasks to demonstrate the effectiveness of algorithms.

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The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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