# Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping

arXiv: https://arxiv.org/abs/2001.07527 · HTML: https://mlsanity.com/paper/2001.07527/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2204.09418/) (2022)
- [Plug and Play, Model-Based Reinforcement Learning](https://mlsanity.com/paper/2108.08960/) (2021)

## Abstract (arXiv)

We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allows for sample-efficient learning on large problems by exploiting a factorization to approximate the value function. Our approach only requires knowledge about the structure of the problem in the form of a dynamic decision network. Using this information, our method learns a model of the environment and performs temporal difference updates which affect multiple joint states and actions at once. Batch updates are additionally performed which efficiently back-propagate knowledge throughout the factored Q-function. Our method outperforms the state-of-the-art algorithm sparse cooperative Q-learning algorithm, both on the well-known SysAdmin benchmark and randomized environments.