16 papers
Accelerating Q-learning through Efficient Value-Sharing across Actions
Prabhat Nagarajan, Brett Daley, Martha White +1
Action values are foundational to many control algorithms such as Q-learning. Therefore, efficient action-value learning is central to reinforcement learning (RL). However, learnin…
Laplacian Representations for Decision-Time Planning
Dikshant Shehmar, Matthew Schlegel, Matthew E. Taylor +1
Planning with a learned model remains a key challenge in model-based reinforcement learning (RL). In decision-time planning, state representations are critical as they must support…
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…
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…
DROGO: Default Representation Objective via Graph Optimization in Reinforcement Learning
Hon Tik Tse, Marlos C. Machado
In computational reinforcement learning, the default representation (DR) and its principal eigenvector have been shown to be effective for a wide variety of applications, including…
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…