1 code implementation • 29 Sep 2020 • Yunshu Du, Garrett Warnell, Assefaw Gebremedhin, Peter Stone, Matthew E. Taylor
In this work, we introduce Lucid Dreaming for Experience Replay (LiDER), a conceptually new framework that allows replay experiences to be refreshed by leveraging the agent's current policy.
1 code implementation • 3 Apr 2019 • Gabriel V. de la Cruz Jr., Yunshu Du, Matthew E. Taylor
Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient.
1 code implementation • 21 Dec 2018 • Gabriel V. de la Cruz, Yunshu Du, Matthew E. Taylor
Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using deep neural networks as function approximators to learn directly from raw input images.
1 code implementation • 5 Dec 2018 • Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar, Mehrdad Farajtabar, Razvan Pascanu, Balaji Lakshminarayanan
One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations.
no code implementations • 12 Sep 2017 • Gabriel V. de la Cruz Jr, Yunshu Du, Matthew E. Taylor
Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using a deep neural network as its function approximator and by learning directly from raw images.