Related papers: Momentum-Based Policy Gradient Methods

Momentum-Based Policy Gradient Methods

URL: http://arxiv.org/abs/2007.06680v2
Date: Thu, 6 Aug 2020 13:34:33 GMT
Title: Momentum-Based Policy Gradient Methods
Authors: Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang
Abstract summary: We propose a class of efficient momentum-based policy gradient methods for the model-free reinforcement learning. In particular, we present a non-adaptive version of IS-MBPG method, which also reaches the best known sample complexity of $O(epsilon-3)$ without any large batches.
Score: 133.53164856723782
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In the paper, we propose a class of efficient momentum-based policy gradient methods for the model-free reinforcement learning, which use adaptive learning rates and do not require any large batches. Specifically, we propose a fast important-sampling momentum-based policy gradient (IS-MBPG) method based on a new momentum-based variance reduced technique and the importance sampling technique. We also propose a fast Hessian-aided momentum-based policy gradient (HA-MBPG) method based on the momentum-based variance reduced technique and the Hessian-aided technique. Moreover, we prove that both the IS-MBPG and HA-MBPG methods reach the best known sample complexity of $O(\epsilon^{-3})$ for finding an $\epsilon$-stationary point of the non-concave performance function, which only require one trajectory at each iteration. In particular, we present a non-adaptive version of IS-MBPG method, i.e., IS-MBPG*, which also reaches the best known sample complexity of $O(\epsilon^{-3})$ without any large batches. In the experiments, we apply four benchmark tasks to demonstrate the effectiveness of our algorithms.

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