Using Deep Reinforcement Learning To Play Atari Space Invaders

My agent playing Atari Space Invaders

TL;DR

  • I was able to teach an RL agent how to play Atari Space Invaders using concepts from both RL and DL.
  • I used OpenAI Gym Retro to create the environment that my agent played in. It’s from an initiative that encouraged DRL design across many different but similar environments.
  • The neural network in this model is used to process frames from the game to understand where objects are and what the agent is doing. It uses 3 convolutional layers and 3 dense (fully-connected) layers to do so.
  • My model trained for 10k steps (~4 hrs) and played decently, but Google DeepMind recommends training for 10M-40M steps for optimal playing.
  • Here is a video of me explaining the project and watching the agent play.
  • This project was a replicate of Nicholas Renotte’s DRL model.

Model Walkthrough

Creating The Environment

‘NOOP’ is no action.
In general, the agent does okay, but that’s just because it’s taking random actions.

Building The DL Model

  • The number of filters (32). The goal was to train the filters so that they could detect objects in the frames, like the enemies.
  • The size of the filters (8x8 units) and the number of strides.
  • The ReLU activation function and the shape of the frame.

Creating The RL Agent

Training and Testing

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15 y/o that loves space, science, tech, and philosophy.

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Chloe Wang

Chloe Wang

15 y/o that loves space, science, tech, and philosophy.

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