57 citations · 230 across the 22 of their papers we have counts for
8 papers · 1 filter
Learning Sparse Representations Incrementally in Deep Reinforcement Learning
J. Fernando Hernandez-Garcia, Richard S. Sutton
Sparse representations have been shown to be useful in deep reinforcement learning for mitigating catastrophic interference and improving the performance of agents in terms of cumu…
Discounted Reinforcement Learning Is Not an Optimization Problem
Abhishek Naik, Roshan Shariff, Niko Yasui +2
Discounted reinforcement learning is fundamentally incompatible with function approximation for control in continuing tasks. It is not an optimization problem in its usual formulat…
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning
Kristopher De Asis, Alan Chan, Silviu Pitis +2
We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a $\textit{fixed…
Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel +11
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabiliti…
Planning with Expectation Models
Yi Wan, Zaheer Abbas, Adam White +2
Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL). However, learning these models can be intractable, particularly when the…
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning
Alex Kearney, Vivek Veeriah, Jaden Travnik +2
There is a long history of using meta learning as representation learning, specifically for determining the relevance of inputs. In this paper, we examine an instance of meta-learn…