91 citations · 144 across the 8 of their papers we have counts for
8 papers
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
The Trajectory Alignment Coefficient in Two Acts: From Reward Tuning to Reward Learning
Calarina Muslimani, Yunshu Du, Kenta Kawamoto +3
The success of reinforcement learning (RL) is fundamentally tied to having a reward function that accurately reflects the task objective. Yet, designing reward functions is notorio…
Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting
Chen-Chen Zong, Zheng-Tao Cao, Hong-Tao Guo +4
Deep neural networks trained with standard cross-entropy loss are more prone to memorize noisy labels, which degrades their performance. Negative learning using complementary label…
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…
Pre-training Neural Networks with Human Demonstrations 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 a deep neural network as its function approximator and by learning dire…