基于python强化学习与深度强化学习的游戏AI训练源码+项目说明+论文+报告.zip

The idea is about proposing a method that combines both knowledge representation and DRL. ## Quick Start - To use GPU, CUDA and cuDNN should be installed correctly. *How to install?* + [CUDA](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html) + [cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/) - To record the final traning model playing the game, ffmpeg should be installed in your system. *How to install?* + [FFmpeg](http://ffmpeg.org/download.html) 1. Install the requirements using: ```bash pip install -r requirements.txt ``` 2. Run the DRIL. ```bash python pong_DRIL.py ``` Run the DQN. ```bash python pong_DQN.py ``` 3. If you need to check how to tweak the parameters. Using: `python pong_DRIL.py -h` or `python pong_DQN.py -h` *However, the Rules-Interposing Decay Models' parameters can only be tweaked in files.* 4. Load the trained model. ```bash python pong_load_model.py ``` *If you need more help about the load model module, please use `python pong_load_model.py -h`* ## Training Results Average Q Values on Pong > ./plotData/Pong_Avg_Q_Values.png Average Rewards on Pong > ./plotData/Pong_Avg_Rewards.png ## TODO The hyperparameters may not be the best for Pong game in both DRIL and DQN. It still needs more explorations. ## References [1] Haodi Zhang, Zihang Gao, Yi Zhou, et al. Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning. arXiv preprint. 2019:1910.09986. [2] Mnih V., Kavukcuoglu K., Silver D., et al. Human-level control through deep reinforcement learning. Nature,2015,518(7540):529-533.