About Deep Reinforcement Learning

Reinforcement Learning is a machine learning approach for teaching agents how to solve tasks by trial and error. The combination of Reinforcement Learning and Deep Learning produces a series of important algorithms. This project will focus on referring to relevant papers and implementing relevant algorithms as far as possible.

This repo aims to implement Deep Reinforcement Learning algorithms using Pytorch.

1.Why do this?

• Implementing all of this algorithms really helps you with your parameter tuning.
• The coding process allows you to better understand the principles of the algorithm.

2.Lists of Algorithms

3.Project Dependencies

• Python >=3.6
• PyTorch >= 1.3.1
• OpenAI Gym

4.Run

Each algorithm is implemented in a single package including:

main.py --A minimal executable example for algorithm  
[algorithm].py --Main body for algorithm implementation   

You can run algorithm from the main.py w.r.t each algorithm's folder

• You can simply type python main.py --help in the algorithm package to view all parameters.

5.References

• Berkeley Deep RL
• Deep RL Bootcamp
• OpenAI's SpinningUp