pytorch_DGN

This is a pytorch implementation of DGN.

Surviving

Here, we propose a multi-agent environment Surviving to evaluate DGN in large-scale situation. There are 100 agents interacting with the environment. Each agent corresponds to one grid and has a vary limited local observation that contains a square view with 3 x 3 grids centered at the agent. The agent could communicate with the neighboring agents in the square region with 7 x 7 grids. At each timestep, each agent can move to one of four neighboring grids or eat the food at its location. Agents begin with 10 health and lose 1 health per step. Eating food could increase the health. If the agent hits 0 health, it gets a reward -0.2, otherwise the reward is 0.4.

Citation

If you are using the codes, please cite our paper.

Jiechuan Jiang, Chen Dun, Tiejun Huang, and Zongqing Lu. Graph convolutional reinforcement learning. ICLR'20.

@inproceedings{jiang2020graph,
    	title={Graph Convolutional Reinforcement Learning},
    	author={Jiang, Jiechuan and Dun, Chen and Huang, Tiejun and Lu, Zongqing},
    	booktitle={ICLR},
    	year={2020}
}