PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm

• URL: http://arxiv.org/abs/2306.06637v1
• Date: Sun, 11 Jun 2023 09:45:31 GMT
• Title: PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm
• Authors: Wensong Bai, Chao Zhang, Yichao Fu, Lingwei Peng, Hui Qian, Bin Dai
• Abstract summary: We propose the first fully push-forward-based DistributionReinforcement Learning algorithm, called Push-forward-based Actor-Critic EncourageR (PACER) PACER establishes a maximum utility value policy gradient and simultaneously leverages the push-forward operator in the construction of both the actor and the critic. Experimental evaluations on various continuous control benchmarks demonstrate the superiority of our algorithm over the state-of-the-art.
• Score: 13.18145235926629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose the first fully push-forward-based Distributional Reinforcement Learning algorithm, called Push-forward-based Actor-Critic EncourageR (PACER). Specifically, PACER establishes a stochastic utility value policy gradient theorem and simultaneously leverages the push-forward operator in the construction of both the actor and the critic. Moreover, based on maximum mean discrepancies (MMD), a novel sample-based encourager is designed to incentivize exploration. Experimental evaluations on various continuous control benchmarks demonstrate the superiority of our algorithm over the state-of-the-art.

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