John Quan

TL;DR: It is shown that it is possible to overcome the limitation of connectionist models and train networks that can maintain expertise on tasks that they have not experienced for a long time and selectively slowing down learning on the weights important for previous tasks.

TL;DR: In this paper, the authors show that it is possible to train networks that can maintain expertise on tasks that they have not experienced for a long time by selectively slowing down learning on the weights important for those tasks.

TL;DR: Prioritized experience replay as mentioned in this paper is a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently, achieving human-level performance across many Atari games.

TL;DR: This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game that offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures and gives initial baseline results for neural networks trained from this data to predict game outcomes and player actions.

TL;DR: A framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently, in Deep Q-Networks, a reinforcement learning algorithm that achieved human-level performance across many Atari games.