Decentralized Multi-Agent Actor-Critic with Generative Inference

2019-10-07 · Kevin Corder, Manuel M. Vindiola, Keith Decker

Recent multi-agent actor-critic methods have utilized centralized training with decentralized execution to address the non-stationarity of co-adapting agents. This training paradigm constrains learning to the centralized phase such that only pre-learned policies may be used during the decentralized phase, which performs poorly when agent communications are delayed, noisy, or disrupted. In this work, we propose a new system that can gracefully handle partially-observable information due to communication disruptions during decentralized execution. Our approach augments the multi-agent actor-critic method's centralized training phase with generative modeling so that agents may infer other agents' observations when provided with locally available context. Our method is evaluated on three tasks that require agents to combine local and remote observations communicated by other agents. We evaluate our approach by introducing both partial observability during decentralized execution, and show that decentralized training on inferred observations performs as well or better than existing actor-critic methods.

📄 PDF Abstract BibTeX arXiv:1910.03058

Code (0)

등록된 구현이 없습니다.

Multi-Agent Actor-Critic with Generative Cooperative Policy Network

2018-10-22 · Heechang Ryu, Hayong Shin, Jinkyoo Park

We propose an efficient multi-agent reinforcement learning approach to derive equilibrium strategies for multi-agents who are participating in a Markov game. Mainly, we are focused on obtaining decentralized policies for…

Multi-agent Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)

F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

2020-04-17 · Wenhao Li, Bo Jin, Xiangfeng Wang, Junchi Yan 외

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complex…

Multi-agent Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)Starcraft+1

Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning

2025-05-24 · Zhiyao Zhang, Myeung Suk Oh, FNU Hairi, Ziyue Luo 외

Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a wide range of applications in practice. …

Decision Making Multi-agent Reinforcement Learning

Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning

2021-02-08 · Xueguang Lyu, Yuchen Xiao, Brett Daley, Christopher Amato

Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity in the multi-agent reinforcement learni…

Misconceptions Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Decentralized Deterministic Multi-Agent Reinforcement Learning

2021-02-19 · Antoine Grosnit, Desmond Cai, Laura Wynter

[Zhang, ICML 2018] provided the first decentralized actor-critic algorithm for multi-agent reinforcement learning (MARL) that offers convergence guarantees. In that work, policies are stochastic and are defined on finite…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)