• Project link: https://github.com/medipixel/rl_algorithms (Pytorch)

Hi, ML redditors all around the world! Recently, I have been studying Reinforcement Learning methods and implementing some of the ideas that could possibly improve my work. Not long ago I took a look at Implicit Quantile Networks for Distributional Reinforcement Learning (Dabney et al., 2018), and one sentence at the end caught my attention.

“Creating a Rainbow-IQN agent could yield even greater improvements on Atari-57.”

So I tried it. As the figure attached in the project readme, it learns Atari Pong incredibly faster than Rainbow as it reaches the perfect score (+21) within just 100 episodes. I am so happy to share this result, even though it is not so enough to evaluate the method’s performance objectively.

Plus, the repository contains other methods as well: A2C, PPO, DDPG, TD3, SAC, from Demonstration, Behaviour Cloning, and so on.

Any feedback will be so appreciated! Thanks 🙂