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sc2_QMIX
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The project to learn the QMIX.
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sc2_QMIX
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# QMIX for StarCraft II ## No. of Edition: 12-16-14-26 The edition of the project uploaded is 12-18-14-22, which has been updated to speed the process of training. This edition have the same effect as the paper saies. ## Description: This project is created for using QMIX algorithm in StarCraft II mini-games. The units in these maps are partial-obs, which will show the advantage of the QMIX. ## Installation known dependencies: Python(3.6.8), StarCraft(3.16.1 or latest version), Pysc2(3.0.0), smac(0.1.0), Pytorch(1.1.0), Numpy(1.17.3) Step 1: Install the StarCraft and Pysc2 from the website:OpenAI Gym(0.10.5://github.com/deepmind/pysc2) Step 2: Install the environment of smac(a modified env for StarCraft II based on Pysc2) from https://github.com/oxwhirl/smac Step 3: Change the Hyper-parameters of the algorithm in 'arguments.py' Step 4: Run the main.py file with 'python main.py --map_name=8m --per_episode_max_len=80' Step 5: Enjoy the model trained by yourself, you can run the command "python enjoy.py --map_name=1c3s5z --old_model_name=./models/1912_190153/" ## Code Structure(update in the future): ./main.py main function of the project ./enjoy.py the file to test the model ./model.py define of the models ./arguments.py the hyper-pars for the project ./replay_buffer.py the memory of the agent ./Q_MIX.py the class for the agent of QMIX ## Command line options ### Environment options --map_name: defines which environment in the MPE is to be used (default: "simple") --per_episode_max_len: maximum length of each episode for the environment (default: It depends on the map that you are training, see from the papers ) --max_episode: total number of training episodes (default: 150000) ### Training options --lr: learning rate for Adam optimizer(default: 5e-4) --gamma: discount factor (default: 0.99) --batch_size: batch size (default: 32 episodes) ### Checkpointing --save_dir: directory where intermediate training results and model will be saved (default: "/models") --fre4save_model: model is saved every time this number of game episodes has been completed (default: 234) --start_save_model: the time when we start to save the model(default: 800) ## Link for blog DeepMind Github: https://github.com/deepmind/pysc2 Another QMIX algorithm:: https://github.com/starry-sky6688/StarCraft The explorer of Game AI: https://zhuanlan.zhihu.com/c_186658689
