Yunhao Tang

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Columbia University

PhD student at Columbia IEOR

Monte-Carlo Tree Search as Regularized Policy Optimization

The combination of Monte-Carlo tree search (MCTS) with deep reinforcemen...
07/24/2020 ∙ by Jean-Bastien Grill, et al. ∙ 4 ∙ share

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Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies

Off-policy learning algorithms have been known to be sensitive to the ch...
06/13/2020 ∙ by Yunhao Tang, et al. ∙ 0 ∙ share

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Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning

We propose a graphical model framework for goal-conditioned RL, with an ...
06/13/2020 ∙ by Yunhao Tang, et al. ∙ 0 ∙ share

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Self-Imitation Learning via Generalized Lower Bound Q-learning

Self-imitation learning motivated by lower-bound Q-learning is a novel a...
06/12/2020 ∙ by Yunhao Tang, et al. ∙ 0 ∙ share

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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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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. ∙ 13 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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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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Yunhao Tang

Featured Co-authors

Remi Munos

Mingyuan Zhou

Michal Valko

Krzysztof Choromanski

Adrian Weller

Vikas Sindhwani

Aldo Pacchiano

Mark Rowland

Anna Choromanska

Michael Jordan

Shipra Agrawal

Monte-Carlo Tree Search as Regularized Policy Optimization

Online Hyper-parameter Tuning in Off-policy Learning via Evolutionary Strategies

Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning

Self-Imitation Learning via Generalized Lower Bound Q-learning

Taylor Expansion Policy Optimization

Discrete Action On-Policy Learning with Action-Value Critic

ES-MAML: Simple Hessian-Free Meta Learning

Reinforcement Learning with Chromatic Networks

Reinforcement Learning for Integer Programming: Learning to Cut

Wasserstein Reinforcement Learning

Variance Reduction for Evolution Strategies via Structured Control Variates

Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes

Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy

Orthogonal Estimation of Wasserstein Distances

Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces

Discretizing Continuous Action Space for On-Policy Optimization

Boosting Trust Region Policy Optimization by Normalizing Flows Policy

Implicit Policy for Reinforcement Learning

Exploration by Distributional Reinforcement Learning

Variational Deep Q Network