57 citations · 230 across the 22 of their papers we have counts for
8 papers · 1 filter
Online Off-policy Prediction
Sina Ghiassian, Andrew Patterson, Martha White +2
This paper investigates the problem of online prediction learning, where learning proceeds continuously as the agent interacts with an environment. The predictions made by the agen…
Predicting Periodicity with Temporal Difference Learning
Kristopher De Asis, Brendan Bennett, Richard S. Sutton
Temporal difference (TD) learning is an important approach in reinforcement learning, as it combines ideas from dynamic programming and Monte Carlo methods in a way that allows for…
Per-decision Multi-step Temporal Difference Learning with Control Variates
Kristopher De Asis, Richard S. Sutton
Multi-step temporal difference (TD) learning is an important approach in reinforcement learning, as it unifies one-step TD learning with Monte Carlo methods in a way where intermed…
Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling
Kenny J. Young, Richard S. Sutton, Shuo Yang
Episodic memory is a psychology term which refers to the ability to recall specific events from the past. We suggest one advantage of this particular type of memory is the ability…
Two geometric input transformation methods for fast online reinforcement learning with neural nets
Sina Ghiassian, Huizhen Yu, Banafsheh Rafiee +1
We apply neural nets with ReLU gates in online reinforcement learning. Our goal is to train these networks in an incremental manner, without the computationally expensive experienc…
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent
Alex Kearney, Vivek Veeriah, Jaden B. Travnik +2
In this paper, we introduce a method for adapting the step-sizes of temporal difference (TD) learning. The performance of TD methods often depends on well chosen step-sizes, yet fe…