Fully-Decentralized MADDPG with Networked Agents
On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization
Abstract (arXiv)
In this paper, we devise three actor-critic algorithms with decentralized training for multi-agent reinforcement learning in cooperative, adversarial, and mixed settings with continuous action spaces. To this goal, we adapt the MADDPG algorithm by applying a networked communication approach between agents. We introduce surrogate policies in order to decentralize the training while allowing for local communication during training. The decentralized algorithms achieve comparable results to the original MADDPG in empirical tests, while reducing computational cost. This is more pronounced with larger numbers of agents.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 8 references that are in the map. First posted on arXiv 9 Mar 2025.
Builds on
6 of 8 references.
References · 6
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
- 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
- 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
- 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
- 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 194
- 2023Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behaviorcited by 7
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- 2026Scalable Neighborhood-Based Multi-Agent Actor-Critic
- 2025Reducing Variance Caused by Communication in Decentralized Multi-agent Deep Reinforcement Learning
- 2024On Centralized Critics in Multi-Agent Reinforcement Learning
- 2024Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
- 2019Decentralized Multi-Agents by Imitation of a Centralized Controller
- 2024A Distributed Primal-Dual Method for Constrained Multi-agent Reinforcement Learning with General Parameterization ↗