UPDATE: our paper has changed since our initial devpost introduction.
INTRODUCTION: We are implementing an existing paper by V Mnih et Al titled Playing Atari with Deep Reinforcement Learning. The goal of this paper is to adapt a Deep Q-learning algorithm to train on a set of 7 Atari 2600 games and compare those results to that obtained by two reinforcement learning algorithms: Sarsa and Contingency and a human result. We will initially be training our DQN on an Atari gym game called breakout - the paper’s DQN with experience replay scored 168 on this game and we hope to get close to this score.
CHALLENGES: We have battled to understand the concept of replay memory detailed in the paper. We also initially selected an algorithm which was very similar to the one completed in hw6 so we’ve had to change our approach to DQN and use an Atari game breakout instead of the cart pole problem from the gym.
INSIGHTS: We’re still in the process of coding our model so we have not been able to provide any results up until this point.
PLAN: Yes we’re on track with our project. What we need to do next is: 1) finish preprocessing our images so that we can pass in batches of 4 images in the correct grayscale (84*84) format; 2) finish coding our DQN algorithm to that we can begin to test our model 3) compare the results of our DQN to that of others and asses our success 4) possibly train our model on other Atari games to see how well it fares against the results found in the paper. For comparative purposes the paper’s version of DQN scored a 168 on the game so
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