Coding a Deep Q Network in PyTorch

Coding a Deep Q Network in PyTorch

In a previous post we covered a quick and dirty introduction to deep Q learning. This covered the conceptual basics: an agent uses a deep neural network to approximate the value of its action-value function, and attempts to maximize its score over time using an off-policy learning strategy. The high level intuition is sufficient to know what’s going on, but now it’s time to dive into the details to actually code up the deep Q network. In this post, we’re…

Read More Read More

A Quick Introduction to Deep Q Learning

A Quick Introduction to Deep Q Learning

Several years ago the Deep Mind team announced that they had designed a new reinforcement learning algorithm capable of beating human level play in multiple games from the Atari library. This new algorithm, deep Q learning, was the marriage of deep neural networks and traditional Q learning. Rather than being a trivial bolt on, deep Q learning is actually an ingenious solution to a difficult problem: how to handle the incredibly large state spaces of even primitive video games? For…

Read More Read More