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[Project] Massively parallel, vectorised implementation of Snake and RL solution

[Project] Massively parallel, vectorised implementation of Snake and RL solution

Hi /r/MachineLearning.

As part of my recent side project to learn about reinforcement learning I’ve created a clone of the classic Snake game as a reinforcement learning environment and solved it with advantage actor-critic. This is one of the warm-ups from OpenAI’s requests for research 2 (https://openai.com/blog/requests-for-research-2/).

You might be thinking this sounds like a very run of the mill introductory RL project. Well here are a few things that I think make it more interesting than just that.

  1. I went completely overboard on the environment. Its implemented in pure PyTorch in a vectorized fashion such that I can run 1000s of environment in parallel on a single machine.
  2. I compare performance of a few architectures, including a model copied from Deepmind’s recent Relational RL paper (spoilers, it doesn’t outcompete the other agents on this very simple task).
  3. I evaluate the performance of an agent trained on a small environment in a larger environment – a limited form of RL transfer learning.

Medium article: https://towardsdatascience.com/learning-to-play-snake-at-1-million-fps-4aae8d36d2f1

Code: https://github.com/oscarknagg/wurm/tree/medium-article-1

Here’s a GIF of one of the final policies:

Processing gif tdsja08fssz21…

I’m currently working on the “Slitherin'” suggestion on OpenAI’s request for research 2.0. Here’s a preliminary GIF.

Processing gif qkerdp6kusz21…

submitted by /u/oscarknagg
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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.