Search Results for author: Zhaohan Daniel Guo

Found 7 papers, 1 papers with code

Directed Exploration for Reinforcement Learning

no code implementations18 Jun 2019 Zhaohan Daniel Guo, Emma Brunskill

Efficient exploration is necessary to achieve good sample efficiency for reinforcement learning in general.

Efficient Exploration reinforcement-learning

Neural Predictive Belief Representations

no code implementations15 Nov 2018 Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Bernardo A. Pires, Rémi Munos

In partially observable domains it is important for the representation to encode a belief state, a sufficient statistic of the observations seen so far.

Decision Making Representation Learning

Sample Efficient Feature Selection for Factored MDPs

no code implementations9 Mar 2017 Zhaohan Daniel Guo, Emma Brunskill

This can result in a much better sample complexity when the in-degree of the necessary features is smaller than the in-degree of all features.


Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation

no code implementations NeurIPS 2017 Zhaohan Daniel Guo, Philip S. Thomas, Emma Brunskill

In addition, we can take advantage of special cases that arise due to options-based policies to further improve the performance of importance sampling.

A PAC RL Algorithm for Episodic POMDPs

no code implementations25 May 2016 Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill

Many interesting real world domains involve reinforcement learning (RL) in partially observable environments.


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