16 citations · 26 across the 13 of their papers we have counts for
4 papers · 1 filter
Learning Representations for Pixel-based Control: What Matters and Why?
Manan Tomar, Utkarsh A. Mishra, Amy Zhang +1
Learning representations for pixel-based control has garnered significant attention recently in reinforcement learning. A wide range of methods have been proposed to enable efficie…
Multi-Agent Advisor Q-Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and sl…
The Atari Data Scraper
Brittany Davis Pierson, Justine Ventura, Matthew E. Taylor
Reinforcement learning has made great strides in recent years due to the success of methods using deep neural networks. However, such neural networks act as a black box, obscuring…
Learning to Shape Rewards using a Game of Two Partners
David Mguni, Taher Jafferjee, Jianhong Wang +9
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engi…