Implementing DeepMind's DQN from Scratch - Project Update

Dive into a 27-minute project update video on implementing DeepMind's DQN from scratch. Explore the workflow, project organization, and thought process behind building a Deep Q-Network. Follow along as the presenter walks through Tensorboard visualizations, explains DQN arguments, and breaks down the main loop for collecting experience and learning. Gain insights into the actor-learner class, visualization techniques using matplotlib and Tensorboard, and learn from analyzing other developers' projects. Perfect for reinforcement learning enthusiasts looking to understand the challenges and intricacies of implementing advanced AI algorithms.

Intro, my other deep learning projects
Patreon
Tensorboard walk-through
Code walk-through - understand DQN arguments
Main loop collecting experience and learning from it
Main actor-learner class
Visualizations matplolib, tensorboard
Analyzing other people's projects