RL Series-Implementation in PyTorch

I decided to implement different Reinforcement Learning algorithms in PyTorch. I try to do it as simple as possible for learning purposes. Here is a list of the algorithms that I hope I can implement: (I try to select the correct name for different techniques as much as I can)
- DQN
- Double DQN
- Dueling DQN
- REINFORCE
- A2C and A3C
- TRPO
- PPO
- DDPG
- SAC
- LQR
Maybe I try a technique on several environments or several techniques on an environment. I also hope that I can implement some model-based RL and multi-agent RL techniques too. I will work on self-driving cars and RL in my PhD and will try to test some of these techniques in some environments such as CARLA or CarRacing gym env.
There are a lot of good resources to learn about RL that I will use and you can find some of them here:
And many more resources that you can find through the internet.
I reviewed these resources through the #100DaysOfMLCode challenge, the RL course at Aalto University and the studies that I’m doing for my PhD. I think that’s it for this post. We will continue with DQN in the next post.
