reinforcement-learning-wechat-jump
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Reinforcement Learning for WeChat Jump
Deep Reinforcement Learning Wechat Jump
End-to-end training Wechat-Jump AI using DDPG algorithm
Environment
- PyTorch
- PyOpenCV
- Android Device(1920 * 1080)
- ADB Tools
Detail
- Using screenshot as neural network input
- The
actor
uses tanh as the activation function -
Critic
uses a linear layer to output Q values - Noise is a normal distribution with a
std=0.2
- Get game state with template matching of OpenCV
Train
python train.py
Infer
python infer.py
Reference
- Continuous control with deep reinforcement learning
- https://github.com/ikostrikov/pytorch-ddpg-naf
- https://github.com/floodsung/wechat_jump_end_to_end