93 citations · 487 across the 42 of their papers we have counts for
6 papers · 1 filter
An Analysis of Action-Value Temporal-Difference Methods That Learn State Values
Brett Daley, Prabhat Nagarajan, Martha White +1
The hallmark feature of temporal-difference (TD) learning is bootstrapping: using value predictions to generate new value predictions. The vast majority of TD methods for control l…
Deep Reinforcement Learning with Gradient Eligibility Traces
Esraa Elelimy, Brett Daley, Andrew Patterson +3
Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for t…
Deep Double Q-learning
Prabhat Nagarajan, Martha White, Marlos C. Machado
Double Q-learning is a classical control algorithm that mitigates the maximization bias of Q-learning. To do so, it explicitly trains two independent action-value functions and use…
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov, Akhil Bagaria, Ziyan Luo +3
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intelligence (AI). Hierarchical reinforcement le…
The Cell Must Go On: Agar.io for Continual Reinforcement Learning
Mohamed A. Mohamed, Kateryna Nekhomiazh, Vedant Vyas +3
Continual reinforcement learning (RL) concerns agents that are expected to learn continually, rather than converge to a policy that is then fixed for evaluation. This setting is we…
Reward-Aware Proto-Representations in Reinforcement Learning
Hon Tik Tse, Siddarth Chandrasekar, Marlos C. Machado
In recent years, the successor representation (SR) has attracted increasing attention in reinforcement learning (RL), and it has been used to address some of its key challenges, su…