9 citations · 9 across the 5 of their papers we have counts for
5 papers
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Alan Chan, Hugo Silva, Sungsu Lim +3
Approximate Policy Iteration (API) algorithms alternate between (approximate) policy evaluation and (approximate) greedification. Many different approaches have been explored for a…
Parameter-free Gradient Temporal Difference Learning
Andrew Jacobsen, Alan Chan
Reinforcement learning lies at the intersection of several challenges. Many applications of interest involve extremely large state spaces, requiring function approximation to enabl…
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., $…
Efficient decorrelation of features using Gramian in Reinforcement Learning
Borislav Mavrin, Daniel Graves, Alan Chan
Learning good representations is a long standing problem in reinforcement learning (RL). One of the conventional ways to achieve this goal in the supervised setting is through regu…
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…