# Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/1710.00336 · HTML: https://mlsanity.com/paper/1710.00336/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2017.

## Built on by (most cited first, 13 of 13)

- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [Solving Continuous Control via Q-learning](https://mlsanity.com/paper/2210.12566/) (2022)
- [Learning Efficient Multi-Agent Cooperative Visual Exploration](https://mlsanity.com/paper/2110.05734/) (2021)
- [Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration](https://mlsanity.com/paper/2301.03398/) (2023)
- [Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution](https://mlsanity.com/paper/2404.04253/) (2024)
- [Adaptive parameter sharing for multi-agent reinforcement learning](https://mlsanity.com/paper/2312.09009/) (2023)
- [Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs](https://mlsanity.com/paper/2101.03273/) (2021)
- [Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering](https://mlsanity.com/paper/2309.11247/) (2023)
- [Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning](https://mlsanity.com/paper/2502.05573/) (2025)
- [Multi-Agent Reinforcement Learning for Joint Police Patrol and Dispatch](https://mlsanity.com/paper/2409.02246/) (2024)
- [Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2406.09755/) (2024)
- [Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective](https://mlsanity.com/paper/2510.09937/) (2025)
- [Decoupling Planning and Control for Instructable Agents](https://mlsanity.com/paper/2608.26788/) (2026)

## Abstract (arXiv)

Deep reinforcement learning for multi-agent cooperation and competition has been a hot topic recently. This paper focuses on cooperative multi-agent problem based on actor-critic methods under local observations settings. Multi agent deep deterministic policy gradient obtained state of art results for some multi-agent games, whereas, it cannot scale well with growing amount of agents. In order to boost scalability, we propose a parameter sharing deterministic policy gradient method with three variants based on neural networks, including actor-critic sharing, actor sharing and actor sharing with partially shared critic. Benchmarks from rllab show that the proposed method has advantages in learning speed and memory efficiency, well scales with growing amount of agents, and moreover, it can make full use of reward sharing and exchangeability if possible.