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marlbenchmark/on-policy: This is the official implementation of Multi-Agent PPO (MAPPO).

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This repository implements MAPPO, a multi-agent variant of PPO. The implementation in this repositorory is used in the paper "The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games" (https://arxiv.org/abs/2103.01955). This repository is heavily based on https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail. We also make the off-policy repo public, please feel free to try that. off-policy link All hyperparameters and training curves are reported in appendix, we would strongly suggest to double check the important factors before runing the code, such as the rollout threads, episode length, ppo epoch, mini-batches, clip term and so on. Besides, we have updated the newest results on google football testbed and suggestions about the episode length and parameter-sharing in appendix, welcome to check that. We have recently noticed that a lot of papers do not reproduce the mappo results correctly, probably due to the rough hyper-parameters description. We have updated training

MAPPO New Update！！！We support SMAC V2 now～ Chao Yu*, Akash Velu*, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu. This repository implements MAPPO, a multi-agent variant of PPO. The implementation in this repositorory is used in the paper "The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games" ( https://arxiv.org/abs/2103.01955 ). This repository is heavily based on https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail . We also make the off-policy repo public, please feel free to try that. off-policy link All hyperparameters and training curves are reporte

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