activity
20172020
most citedEffective sketching methods for value function approximation

2 citations · 4 across the 3 of their papers we have counts for

collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG20202 cited

Frequency-based Search-control in Dyna

Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand

Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. In particular, Dyna is an elegant model-based architectu…

cs.LG2019

Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online

Yangchen Pan, Kirby Banman, Martha White

Recent work has shown that sparse representations -- where only a small percentage of units are active -- can significantly reduce interference. Those works, however, relied on rel…

cs.LG2019

Hill Climbing on Value Estimates for Search-control in Dyna

Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand +1

Dyna is an architecture for model-based reinforcement learning (RL), where simulated experience from a model is used to update policies or value functions. A key component of Dyna…

cs.LG2018

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control

Yangchen Pan, Amir-massoud Farahmand, Martha White +3

Recent work has shown that reinforcement learning (RL) is a promising approach to control dynamical systems described by partial differential equations (PDE). This paper shows how…

cs.LG20172 cited

Effective sketching methods for value function approximation

Yangchen Pan, Erfan Sadeqi Azer, Martha White

High-dimensional representations, such as radial basis function networks or tile coding, are common choices for policy evaluation in reinforcement learning. Learning with such high…