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Repo containing code for multi-agent deep reinforcement learning (MADRL).
| Date | Stars |
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| 2026-07-31 | 751 |
| 2026-08-05 | 750 |
| 2026-08-06 | 750 |
| 2026-08-07 | 750 |
| 2026-08-14 | 751 |
| 2026-08-15 | 751 |
| 2026-08-18 | 752 |
| 2026-09-13 | 753 |
| 2026-09-20 | 753 |
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# MADRL **Note**: **A maintained version of two of these environments with various fixes is included with PettingZoo (https://github.com/Farama-Foundation/PettingZoo), https://pettingzoo.farama.org/environments/sisl/)** This package provides implementations of the following multi-agent reinforcement learning environemnts: - [Pursuit Evastion](https://github.com/sisl/MADRL/blob/master/madrl_environments/pursuit/pursuit_evade.py) - [Waterworld](https://github.com/sisl/MADRL/blob/master/madrl_environments/pursuit/waterworld.py) - [Multi-Agent Walker](https://github.com/sisl/MADRL/blob/master/madrl_environments/walker/multi_walker.py) - [Multi-Ant](https://github.com/sisl/MADRL/blob/master/madrl_environments/mujoco/ant/multi_ant.py) ## Requirements This package requires both [OpenAI Gym](https://github.com/openai/gym) and a forked version of [rllab](https://github.com/rejuvyesh/rllab/tree/multiagent) (the multiagent branch). There are a number of other requirements which can be found in `rllab/environment.yml` file if using `anaconda` distribution. ## Setup The easiest way to install MADRL and its dependencies is to perform a recursive clone of this repository. ```bash git clone --recursive [email protected]:sisl/MADRL.git ``` Then, add directories to `PYTHONPATH` ```bash export PYTHONPATH=$(pwd):$(pwd)/rltools:$(pwd)/rllab:$PYTHONPATH ``` Install the required dependencies. Good idea is to look into `rllab/environment.yml` file if using `anaconda` distribution. ## Usage Example run with curriculum: ```bash python3 runners/run_multiwalker.py rllab \ # Use rllab for training --control decentralized \ # Decentralized training protocol --policy_hidden 100,50,25 \ # Set MLP policy hidden layer sizes --n_iter 200 \ # Number of iterations --n_walkers 2 \ # Starting number of walkers --batch_size 24000 \ # Number of rollout waypoints --curriculum lessons/multiwalker/env.yaml ``` ## Details Policy definitions exist in `rllab/sandbox/rocky/tf/policies`. ## Citation Please cite the accompanied paper, if you find this useful: ``` @inproceedings{gupta2017cooperative, title={Cooperative multi-agent control using deep reinforcement learning}, author={Gupta, Jayesh K and Egorov, Maxim and Kochenderfer, Mykel}, booktitle={International Conference on Autonomous Agents and Multiagent Systems}, pages={66--83}, year={2017}, organization={Springer} } ```
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:73beb3c615f26978, desc:multi-agent, desc:multi agent