# Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation

arXiv: https://arxiv.org/abs/2003.09540 · HTML: https://mlsanity.com/paper/2003.09540/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Dynamic Handover: Throw and Catch with Bimanual Hands](https://mlsanity.com/paper/2309.05655/) (2023)
- [Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/2103.04972/) (2021)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Byzantine-Robust Online and Offline Distributed Reinforcement Learning](https://mlsanity.com/paper/2206.00165/) (2022)
- [Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing](https://mlsanity.com/paper/2411.07104/) (2024)
- [Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation](https://mlsanity.com/paper/2305.06446/) (2023)
- [Negotiated Reasoning: On Provably Addressing Relative Over-Generalization](https://mlsanity.com/paper/2306.05353/) (2023)

## Abstract (arXiv)

We consider solving a cooperative multi-robot object manipulation task using reinforcement learning (RL). We propose two distributed multi-agent RL approaches: distributed approximate RL (DA-RL), where each agent applies Q-learning with individual reward functions; and game-theoretic RL (GT-RL), where the agents update their Q-values based on the Nash equilibrium of a bimatrix Q-value game. We validate the proposed approaches in the setting of cooperative object manipulation with two simulated robot arms. Although we focus on a small system of two agents in this paper, both DA-RL and GT-RL apply to general multi-agent systems, and are expected to scale well to large systems.