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[P] Rainbow-IQN, that reaches the perfect score (+21) on Atari Pong within 100 episodes!

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 🙂

submitted by /u/Curt-Park
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.