https://tomroth.dev/tags/dqn/Recent content in dqn on Tom RothHugo -- gohugo.ioenThu, 01 Nov 2018 00:00:00 +0000https://tomroth.dev/dqn-nnet/Thu, 01 Nov 2018 00:00:00 +0000https://tomroth.dev/dqn-nnet/After building the basic DQN, I started trying some different neural network architectures. I found vastly different results between them. Spoiler: more complex doesn’t equal better. Almost the opposite. Here are some results for the rewards obtained in each episode. I trained these models on cartpole-v1. Each was trained for 1000 episodes. Disclaimer: I did only one run with each architectures. I kept other hyperparameters the same as the previous post, but there are likely some interaction effects unaccounted for.https://tomroth.dev/dqn-simple/Wed, 31 Oct 2018 00:00:00 +0000https://tomroth.dev/dqn-simple/Click the image above to see the source code. Last time, we established some theory for our DQN. Now it’s time to implement it. We won’t build a full DQN from the DeepMind paper, not yet. Instead we’ll build a simplified version. I call it the basic DQN. The basic DQN is the same as the full DQN, but missing a target network and reward clipping. We’ll get to that in the next post.https://tomroth.dev/dqn-basics/Tue, 23 Oct 2018 00:00:00 +0000https://tomroth.dev/dqn-basics/In February 2015, Google Brain published a paper combining deep neural networks and reinforcement learning for the first time - called DQN (deep q-network). It was a landmark moment. DQN was the first algorithm that could successfully play a wide range of Atari games. Other algorithms at the time could perform well at a single game but couldn’t generalise across games. Impressively, DQN was able to perform above human level at a range of Atari games using only the screen pixels as input.