Pith. sign in

Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space

· 2019 · cs.LG · arXiv 1903.01344

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
open full Pith review browse 2 citing papers arXiv PDF
abstract

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.

fields

cs.LG 2

years

2026 2

verdicts

UNVERDICTED 2

representative citing papers

Revisiting Action Factorization for Complex Action Spaces

cs.LG · 2026-06-25 · unverdicted · novelty 5.0

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-MIX variants outperforming other PPO approaches.

citing papers explorer

Showing 2 of 2 citing papers.

  • Revisiting Action Factorization for Complex Action Spaces cs.LG · 2026-06-25 · unverdicted · none · ref 9 · internal anchor

    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-MIX variants outperforming other PPO approaches.

  • TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning cs.LG · 2026-06-16 · unverdicted · none · ref 10 · internal anchor

    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 equilibrium with O(sqrt(K)) violation bounds and large reductions in training violation