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Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space

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arxiv 1903.01344 v3 pith:XCJ32V7K submitted 2019-03-04 cs.LG cs.AIstat.ML

Zhou Fan , Rui Su , Weinan Zhang , Yong Yu This is my paper · ORCID
classification cs.LGcs.AIstat.ML
keywords actionhybridparameterizedspaceactor-criticarchitectureh-ppolearning
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In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks. While this paper is mainly focused on parameterized action space, the proposed architecture, which we call hybrid actor-critic, can be extended for more general action spaces which has a hierarchical structure. We present an instance of the hybrid actor-critic architecture based on proximal policy optimization (PPO), which we refer to as hybrid proximal policy optimization (H-PPO). Our experiments test H-PPO on a collection of tasks with parameterized action space, where H-PPO demonstrates superior performance over previous methods of parameterized action reinforcement learning.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Revisiting Action Factorization for Complex Action Spaces

    cs.LG 2026-06 unverdicted novelty 5.0 of 10

    Comparative study of action factorization methods for hybrid action spaces across PPO/SAC/DQN finds branching dueling architectures effective and auto-regressive methods highest performing, with new VDN-PPO and PPO-MI...

  2. TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning

    cs.LG 2026-06 unverdicted novelty 5.0 of 10

    TRIDENT is a MARL framework using Richardson-Romberg gradient correction, Lyapunov-constrained trust-region updates, and a physics-informed residual critic that claims O(1/sqrt(K)) convergence to constrained Nash equi...

  3. Learn A Flexible Exploration Model for Parameterized Action Markov Decision Processes

    cs.LG 2025-01 reject novelty 4.0 of 10

    FLEXplore combines an L2 dynamics loss with a Wasserstein-style critic loss, FGSM reward smoothing, and a mutual-information auxiliary reward to improve sample efficiency in parameterized-action MDPs.

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