DQN for Atari Breakout from Scratch

Deep Q-Networks (DQNs) were a milestone in deep reinforcement learning, famously solving Atari games directly from pixels.
For this project, I built a from-scratch implementation of key RL techniques to train an agent to play Atari Breakout, including:

• Deep Q-Learning – iconic approach for learning Atari games directly from pixels
• Double Q-Learning – reducing overestimation bias in Q-learning
• Prioritized Replay – sampling important experiences more frequently

I noticed there are surprisingly few clear explanations of how to implement these ideas step-by-step. Thus, I tried to write something useful and if you are interested, feel free to read the blog post here.

You can also explore the code on GitHub.

References

Here are the main papers that inspired this project:

• Playing Atari with Deep Reinforcement Learning (Mnih et al., 2013)
• Double Q-learning (van Hasselt et al., 2015)
• Prioritized Experience Replay (Schaul et al., 2016)