Torch Dueling Deep Q-Networks

Deep Q-networks (DQNs) [1] have reignited interest in neural networks for reinforcement learning, proving their abilities on the challenging Arcade Learning Environment (ALE) benchmark [2]. The ALE is a reinforcement learning interface for over 50 video games for the Atari 2600; with a single architecture and choice of hyperparameters the DQN was able to achieve superhuman scores on over half of these games. The original work has now been superseded with several advancements, several of which can be found on GitHub. As training on the ALE can take over a week on a GPU, the code is also set up to learn how to play a simpler game of catch in a couple of hours on a CPU. Most recent deep learning research has focused around supervised learning, which involves finding a mapping from input data \(x\) to target data \(y\).