6 citations · 6 across the 7 of their papers we have counts for
7 papers
The Open Ant: A Robot Platform for Reinforcement Learning Research
Elena Sorina Lupu, Patrick Spieler, Khurram Javed +4
Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology remains rooted in simulations. The predom…
Extending Differential Temporal Difference Methods for Episodic Problems
Kris De Asis, Mohamed Elsayed, Jiamin He
Differential temporal difference (TD) methods are value-based reinforcement learning algorithms that have been proposed for infinite-horizon problems. They rely on reward centering…
Value-aware Importance Weighting for Off-policy Reinforcement Learning
Kristopher De Asis, Eric Graves, Richard S. Sutton
Importance sampling is a central idea underlying off-policy prediction in reinforcement learning. It provides a strategy for re-weighting samples from a distribution to obtain unbi…
Inverse Policy Evaluation for Value-based Sequential Decision-making
Alan Chan, Kris de Asis, Richard S. Sutton
Value-based methods for reinforcement learning lack generally applicable ways to derive behavior from a value function. Many approaches involve approximate value iteration (e.g., $…
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…
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…