b'Yunhao Tang'

research

∙ 05/29/2023

VA-learning as a more efficient alternative to Q-learning

In reinforcement learning, the advantage function is critical for policy...

0 Yunhao Tang, et al. ∙

research

∙ 05/29/2023

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm

Multi-step learning applies lookahead over multiple time steps and has p...

0 Yunhao Tang, et al. ∙

research

∙ 05/29/2023

Towards a Better Understanding of Representation Dynamics under TD-learning

TD-learning is a foundation reinforcement learning (RL) algorithm for va...

0 Yunhao Tang, et al. ∙

research

∙ 05/28/2023

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation

We study the problem of temporal-difference-based policy evaluation in r...

0 Mark Rowland, et al. ∙

research

∙ 05/22/2023

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice

Mirror descent value iteration (MDVI), an abstraction of Kullback-Leible...

0 Toshinori Kitamura, et al. ∙

research

∙ 05/01/2023

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition

Representation learning and exploration are among the key challenges for...

0 Yash Chandak, et al. ∙

research

∙ 03/14/2023

Fast Rates for Maximum Entropy Exploration

We consider the reinforcement learning (RL) setting, in which the agent ...

0 Daniil Tiapkin, et al. ∙

research

∙ 02/09/2023

The Edge of Orthogonality: A Simple View of What Makes BYOL Tick

Self-predictive unsupervised learning methods such as BYOL or SimSiam ha...

0 Pierre H. Richemond, et al. ∙

research

∙ 01/11/2023

An Analysis of Quantile Temporal-Difference Learning

We analyse quantile temporal-difference learning (QTD), a distributional...

0 Mark Rowland, et al. ∙

research

∙ 12/06/2022

Understanding Self-Predictive Learning for Reinforcement Learning

We study the learning dynamics of self-predictive learning for reinforce...

0 Yunhao Tang, et al. ∙

research

∙ 07/15/2022

The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning

We study the multi-step off-policy learning approach to distributional R...

0 Yunhao Tang, et al. ∙

research

∙ 06/16/2022

BYOL-Explore: Exploration by Bootstrapped Prediction

We present BYOL-Explore, a conceptually simple yet general approach for ...

0 Zhaohan Daniel Guo, et al. ∙

research

∙ 05/27/2022

KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal

In this work, we consider and analyze the sample complexity of model-fre...

6 Tadashi Kozuno, et al. ∙

research

∙ 05/16/2022

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses

We propose the Bayes-UCBVI algorithm for reinforcement learning in tabul...

0 Daniil Tiapkin, et al. ∙

research

∙ 03/30/2022

Marginalized Operators for Off-policy Reinforcement Learning

In this work, we propose marginalized operators, a new class of off-poli...

0 Yunhao Tang, et al. ∙

research

∙ 12/14/2021

Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning

Despite the empirical success of meta reinforcement learning (meta-RL), ...

0 Yunhao Tang, et al. ∙

research

∙ 06/24/2021

Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation

Model-agnostic meta-reinforcement learning requires estimating the Hessi...

0 Yunhao Tang, et al. ∙

research

∙ 06/11/2021

Taylor Expansion of Discount Factors

In practical reinforcement learning (RL), the discount factor used for e...

0 Yunhao Tang, et al. ∙

research

∙ 02/27/2021

Revisiting Peng's Q(λ) for Modern Reinforcement Learning

Off-policy multi-step reinforcement learning algorithms consist of conse...

0 Tadashi Kozuno, et al. ∙

research

∙ 02/08/2021

Unlocking Pixels for Reinforcement Learning via Implicit Attention

There has recently been significant interest in training reinforcement l...

1 Krzysztof Choromanski, et al. ∙

research

∙ 01/19/2021

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...

16 Xingyou Song, et al. ∙

research

∙ 07/24/2020

Monte-Carlo Tree Search as Regularized Policy Optimization

The combination of Monte-Carlo tree search (MCTS) with deep reinforcemen...

5 Jean-Bastien Grill, et al. ∙

research

∙ 06/13/2020

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

Off-policy learning algorithms have been known to be sensitive to the ch...

1 Yunhao Tang, et al. ∙

research

∙ 06/13/2020

Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning

We propose a graphical model framework for goal-conditioned RL, with an ...

1 Yunhao Tang, et al. ∙

research

∙ 06/12/2020

Self-Imitation Learning via Generalized Lower Bound Q-learning

Self-imitation learning motivated by lower-bound Q-learning is a novel a...

1 Yunhao Tang, et al. ∙

research

∙ 03/13/2020

Taylor Expansion Policy Optimization

In this work, we investigate the application of Taylor expansions in rei...

37 Yunhao Tang, et al. ∙

research

∙ 02/10/2020

Discrete Action On-Policy Learning with Action-Value Critic

Reinforcement learning (RL) in discrete action space is ubiquitous in re...

14 Yuguang Yue, et al. ∙

research

∙ 09/25/2019

ES-MAML: Simple Hessian-Free Meta Learning

We introduce ES-MAML, a new framework for solving the model agnostic met...

16 Xingyou Song, et al. ∙

research

∙ 07/10/2019

Reinforcement Learning with Chromatic Networks

We present a new algorithm for finding compact neural networks encoding ...

6 Xingyou Song, et al. ∙

research

∙ 06/11/2019

Reinforcement Learning for Integer Programming: Learning to Cut

Integer programming (IP) is a general optimization framework widely appl...

4 Yunhao Tang, et al. ∙

research

∙ 06/11/2019

Wasserstein Reinforcement Learning

We propose behavior-driven optimization via Wasserstein distances (WDs) ...

1 Aldo Pacchiano, et al. ∙

research

∙ 05/29/2019

Variance Reduction for Evolution Strategies via Structured Control Variates

Evolution Strategies (ES) are a powerful class of blackbox optimization ...

1 Yunhao Tang, et al. ∙

research

∙ 05/29/2019

Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes

We propose a new class of structured methods for Monte Carlo (MC) sampli...

8 Krzysztof Choromanski, et al. ∙

research

∙ 03/13/2019

Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy

Due to the high variance of policy gradients, on-policy optimization alg...

5 Yunhao Tang, et al. ∙

research

∙ 03/09/2019

Orthogonal Estimation of Wasserstein Distances

Wasserstein distances are increasingly used in a wide variety of applica...

7 Mark Rowland, et al. ∙

research

∙ 03/07/2019

Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces

We present a new algorithm ASEBO for conducting optimization of high-dim...

12 Krzysztof Choromanski, et al. ∙

research

∙ 01/29/2019

Discretizing Continuous Action Space for On-Policy Optimization

In this work, we show that discretizing action space for continuous cont...

5 Yunhao Tang, et al. ∙

research

∙ 09/27/2018

Boosting Trust Region Policy Optimization by Normalizing Flows Policy

We propose to improve trust region policy search with normalizing flows ...

5 Yunhao Tang, et al. ∙

research

∙ 06/10/2018

Implicit Policy for Reinforcement Learning

We introduce Implicit Policy, a general class of expressive policies tha...

1 Yunhao Tang, et al. ∙

research

∙ 05/04/2018

Exploration by Distributional Reinforcement Learning

We propose a framework based on distributional reinforcement learning an...

1 Yunhao Tang, et al. ∙

research

∙ 11/30/2017

Variational Deep Q Network

We propose a framework that directly tackles the probability distributio...

1 Yunhao Tang, et al. ∙

Yunhao Tang

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