93 citations · 487 across the 42 of their papers we have counts for
8 papers · 1 filter
GVFs in the Real World: Making Predictions Online for Water Treatment
Muhammad Kamran Janjua, Haseeb Shah, Martha White +3
In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. Developing such a prediction system is a criti…
Harnessing Discrete Representations For Continual Reinforcement Learning
Edan Meyer, Adam White, Marlos C. Machado
Reinforcement learning (RL) agents make decisions using nothing but observations from the environment, and consequently, heavily rely on the representations of those observations.…
Directions of Curvature as an Explanation for Loss of Plasticity
Alex Lewandowski, Haruto Tanaka, Dale Schuurmans +1
Loss of plasticity is a phenomenon in which neural networks lose their ability to learn from new experience. Despite being empirically observed in several problem settings, little…
AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning
Subhojeet Pramanik, Esraa Elelimy, Marlos C. Machado +1
In this paper we investigate transformer architectures designed for partially observable online reinforcement learning. The self-attention mechanism in the transformer architecture…
Proper Laplacian Representation Learning
Diego Gomez, Michael Bowling, Marlos C. Machado
The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly…
Loss of Plasticity in Continual Deep Reinforcement Learning
Zaheer Abbas, Rosie Zhao, Joseph Modayil +2
The ability to learn continually is essential in a complex and changing world. In this paper, we characterize the behavior of canonical value-based deep reinforcement learning (RL)…
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning
Brett Daley, Martha White, Christopher Amato +1
Off-policy learning from multistep returns is crucial for sample-efficient reinforcement learning, but counteracting off-policy bias without exacerbating variance is challenging. C…
Deep Laplacian-based Options for Temporally-Extended Exploration
Martin Klissarov, Marlos C. Machado
Selecting exploratory actions that generate a rich stream of experience for better learning is a fundamental challenge in reinforcement learning (RL). An approach to tackle this pr…