57 citations · 230 across the 22 of their papers we have counts for
6 papers · 1 filter
Average-Reward Learning and Planning with Options
Yi Wan, Abhishek Naik, Richard S. Sutton
We extend the options framework for temporal abstraction in reinforcement learning from discounted Markov decision processes (MDPs) to average-reward MDPs. Our contributions includ…
An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment
Sina Ghiassian, Richard S. Sutton
Many off-policy prediction learning algorithms have been proposed in the past decade, but it remains unclear which algorithms learn faster than others. We empirically compare 11 of…
An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task
Sina Ghiassian, Richard S. Sutton
Off-policy prediction -- learning the value function for one policy from data generated while following another policy -- is one of the most challenging subproblems in reinforcemen…
Planning with Expectation Models for Control
Katya Kudashkina, Yi Wan, Abhishek Naik +1
In model-based reinforcement learning (MBRL), Wan et al. (2019) showed conditions under which the environment model could produce the expectation of the next feature vector rather…
Scalable Online Recurrent Learning Using Columnar Neural Networks
Khurram Javed, Martha White, Rich Sutton
Structural credit assignment for recurrent learning is challenging. An algorithm called RTRL can compute gradients for recurrent networks online but is computationally intractable…
Does the Adam Optimizer Exacerbate Catastrophic Forgetting?
Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton
Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon. Despit…