1 citations · 1 across the 1 of their papers we have counts for
4 papers
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy
Yunshu Du, Garrett Warnell, Assefaw Gebremedhin +2
Experience replay (ER) improves the data efficiency of off-policy reinforcement learning (RL) algorithms by allowing an agent to store and reuse its past experiences in a replay bu…
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning
Gabriel V. de la Cruz, Yunshu Du, Matthew E. Taylor
Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient. One reason is that a DRL agent learns both the feature and the policy tabula rasa. Integrating prior…
Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning
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 r…
Adapting Auxiliary Losses Using Gradient Similarity
Yunshu Du, Wojciech M. Czarnecki, Siddhant M. Jayakumar +3
One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations. However, it is not always trivia…