Yunhao Tang

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Columbia University

PhD student at Columbia IEOR

Taylor Expansion Policy Optimization

In this work, we investigate the application of Taylor expansions in rei...
03/13/2020 ∙ by Yunhao Tang, et al. ∙ 36 ∙ share

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ES-MAML: Simple Hessian-Free Meta Learning

We introduce ES-MAML, a new framework for solving the model agnostic met...
09/25/2019 ∙ by Xingyou Song, et al. ∙ 15 ∙ share

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Discrete Action On-Policy Learning with Action-Value Critic

Reinforcement learning (RL) in discrete action space is ubiquitous in re...
02/10/2020 ∙ by Yuguang Yue, et al. ∙ 12 ∙ share

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Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces

We present a new algorithm ASEBO for conducting optimization of high-dim...
03/07/2019 ∙ by Krzysztof Choromanski, et al. ∙ 11 ∙ share

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Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes

We propose a new class of structured methods for Monte Carlo (MC) sampli...
05/29/2019 ∙ by Krzysztof Choromanski, et al. ∙ 7 ∙ share

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Orthogonal Estimation of Wasserstein Distances

Wasserstein distances are increasingly used in a wide variety of applica...
03/09/2019 ∙ by Mark Rowland, et al. ∙ 6 ∙ share

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Reinforcement Learning with Chromatic Networks

We present a new algorithm for finding compact neural networks encoding ...
07/10/2019 ∙ by Xingyou Song, et al. ∙ 5 ∙ share

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Boosting Trust Region Policy Optimization by Normalizing Flows Policy

We propose to improve trust region policy search with normalizing flows ...
09/27/2018 ∙ by Yunhao Tang, et al. ∙ 4 ∙ share

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Discretizing Continuous Action Space for On-Policy Optimization

In this work, we show that discretizing action space for continuous cont...
01/29/2019 ∙ by Yunhao Tang, et al. ∙ 4 ∙ share

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Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy

Due to the high variance of policy gradients, on-policy optimization alg...
03/13/2019 ∙ by Yunhao Tang, et al. ∙ 4 ∙ share

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Reinforcement Learning for Integer Programming: Learning to Cut

Integer programming (IP) is a general optimization framework widely appl...
06/11/2019 ∙ by Yunhao Tang, et al. ∙ 3 ∙ share

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Variational Deep Q Network

We propose a framework that directly tackles the probability distributio...
11/30/2017 ∙ by Yunhao Tang, et al. ∙ 0 ∙ share

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Exploration by Distributional Reinforcement Learning

We propose a framework based on distributional reinforcement learning an...
05/04/2018 ∙ by Yunhao Tang, et al. ∙ 0 ∙ share

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Implicit Policy for Reinforcement Learning

We introduce Implicit Policy, a general class of expressive policies tha...
06/10/2018 ∙ by Yunhao Tang, et al. ∙ 0 ∙ share

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Wasserstein Reinforcement Learning

We propose behavior-driven optimization via Wasserstein distances (WDs) ...
06/11/2019 ∙ by Aldo Pacchiano, et al. ∙ 0 ∙ share

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Variance Reduction for Evolution Strategies via Structured Control Variates

Evolution Strategies (ES) are a powerful class of blackbox optimization ...
05/29/2019 ∙ by Yunhao Tang, et al. ∙ 0 ∙ share

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Yunhao Tang

Featured Co-authors

Remi Munos

Mingyuan Zhou

Krzysztof Choromanski

Michal Valko

Vikas Sindhwani

Adrian Weller

Anna Choromanska

Mark Rowland

Aldo Pacchiano

Michael Jordan

Shipra Agrawal

Taylor Expansion Policy Optimization

ES-MAML: Simple Hessian-Free Meta Learning

Discrete Action On-Policy Learning with Action-Value Critic

Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces

Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes

Orthogonal Estimation of Wasserstein Distances

Reinforcement Learning with Chromatic Networks

Boosting Trust Region Policy Optimization by Normalizing Flows Policy

Discretizing Continuous Action Space for On-Policy Optimization

Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy

Reinforcement Learning for Integer Programming: Learning to Cut

Variational Deep Q Network

Exploration by Distributional Reinforcement Learning

Implicit Policy for Reinforcement Learning

Wasserstein Reinforcement Learning

Variance Reduction for Evolution Strategies via Structured Control Variates