Yunhao Tang

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Columbia University

PhD student at Columbia IEOR

Unlocking Pixels for Reinforcement Learning via Implicit Attention

There has recently been significant interest in training reinforcement l...
02/08/2021 ∙ by Krzysztof Choromanski, et al. ∙ 1 ∙ share

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ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning

We introduce ES-ENAS, a simple neural architecture search (NAS) algorith...
01/19/2021 ∙ by Xingyou Song, et al. ∙ 16 ∙ share

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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. ∙ 5 ∙ 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. ∙ 1 ∙ 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. ∙ 1 ∙ 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. ∙ 1 ∙ 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. ∙ 37 ∙ 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. ∙ 14 ∙ 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. ∙ 16 ∙ 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. ∙ 6 ∙ 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. ∙ 4 ∙ 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. ∙ 1 ∙ 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. ∙ 1 ∙ 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. ∙ 8 ∙ 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. ∙ 5 ∙ 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. ∙ 7 ∙ 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. ∙ 12 ∙ 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. ∙ 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. ∙ 5 ∙ 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. ∙ 1 ∙ 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. ∙ 1 ∙ 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. ∙ 1 ∙ share

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Yunhao Tang

Featured Co-authors

Remi Munos

Mingyuan Zhou

Michal Valko

Krzysztof Choromanski

Adrian Weller

Vikas Sindhwani

Aldo Pacchiano

Anna Choromanska

Mark Rowland

Xingyou Song

Michael Jordan

Unlocking Pixels for Reinforcement Learning via Implicit Attention

ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning

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