# An Improved Multi-Agent Algorithm for Cooperative and Competitive Environments by Identifying and Encouraging Cooperation among Agents

arXiv: https://arxiv.org/abs/2508.14131 · HTML: https://mlsanity.com/paper/2508.14131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Independent Policy Gradient Methods for Competitive Reinforcement Learning](https://mlsanity.com/paper/2101.04233/) (2021)
- [Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1906.04737/) (2019)
- [Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.00136/) (2021)
- [Imitation Learning with Concurrent Actions in 3D Games](https://mlsanity.com/paper/1803.05402/) (2018)
- [Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration](https://mlsanity.com/paper/2301.03398/) (2023)
- [Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention](https://mlsanity.com/paper/2304.12653/) (2023)
- [Multi-Agent Deep Deterministic Policy Gradient Algorithm for Peer-to-Peer Energy Trading Considering Distribution Network Constraints](https://mlsanity.com/paper/2108.09053/) (2021)

## Abstract (arXiv)

We propose an improved algorithm by identifying and encouraging cooperative behavior in multi-agent environments. First, we analyze the shortcomings of existing algorithms in addressing multi-agent reinforcement learning problems. Then, based on the existing algorithm MADDPG, we introduce a new parameter to increase the reward that an agent can obtain when cooperative behavior among agents is identified. Finally, we compare our improved algorithm with MADDPG in environments from PettingZoo. The results show that the new algorithm helps agents achieve both higher team rewards and individual rewards.