4 papers · 1 filter
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…
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…