62 citations · 286 across the 40 of their papers we have counts for
40 papers
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning
Zihan Zhang, Yuhang Jiang, Yuan Zhou +1
In this paper, we study the episodic reinforcement learning (RL) problem modeled by finite-horizon Markov Decision Processes (MDPs) with constraint on the number of batches. The mu…
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Zhizhou Ren, Ruihan Guo, Yuan Zhou +1
Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to fut…
Fairness-aware Online Price Discrimination with Nonparametric Demand Models
Xi Chen, Jiameng Lyu, Xuan Zhang +1
Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the…
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits
Zihan Zhang, Xiangyang Ji, Yuan Zhou
We study the optimal batch-regret tradeoff for batch linear contextual bandits. For any batch number , number of actions , time horizon , and dimension , we provide an…
Coordinate-wise Control Variates for Deep Policy Gradients
Yuanyi Zhong, Yuan Zhou, Jian Peng
The control variates (CV) method is widely used in policy gradient estimation to reduce the variance of the gradient estimators in practice. A control variate is applied by subtrac…
Off-Policy Reinforcement Learning with Delayed Rewards
Beining Han, Zhizhou Ren, Zuofan Wu +2
We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-world tasks, instant rewards are often not readily accessible or even defined immediately af…
TSEC: a framework for online experimentation under experimental constraints
Simon Mak, Yuanshuo Zhou, Lavonne Hoang +1
Thompson sampling is a popular algorithm for solving multi-armed bandit problems, and has been applied in a wide range of applications, from website design to portfolio optimizatio…
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity
Tanmay Gangwani, Jian Peng, Yuan Zhou
Quality-Diversity (QD) is a concept from Neuroevolution with some intriguing applications to Reinforcement Learning. It facilitates learning a population of agents where each membe…
Learning Guidance Rewards with Trajectory-space Smoothing
Tanmay Gangwani, Yuan Zhou, Jian Peng
Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that…
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
Yicheng Luo, Antonio Filieri, Yuan Zhou
Probabilistic software analysis aims at quantifying the probability of a target event occurring during the execution of a program processing uncertain incoming data or written itse…
Bayesian Policy Search for Stochastic Domains
David Tolpin, Yuan Zhou, Hongseok Yang
AI planning can be cast as inference in probabilistic models, and probabilistic programming was shown to be capable of policy search in partially observable domains. Prior work int…
Probabilistic Programs with Stochastic Conditioning
David Tolpin, Yuan Zhou, Tom Rainforth +1
We tackle the problem of conditioning probabilistic programs on distributions of observable variables. Probabilistic programs are usually conditioned on samples from the joint data…
Efficient Competitive Self-Play Policy Optimization
Yuanyi Zhong, Yuan Zhou, Jian Peng
Reinforcement learning from self-play has recently reported many successes. Self-play, where the agents compete with themselves, is often used to generate training data for iterati…
Near-Optimal MNL Bandits Under Risk Criteria
Guangyu Xi, Chao Tao, Yuan Zhou
We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria. Unlike the ordinary expected revenue, risk criteria are more general go…
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong, Yingkai Li, Qin Zhang +1
We study multinomial logit bandit with limited adaptivity, where the algorithms change their exploration actions as infrequently as possible when achieving almost optimal minimax r…
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design
Yufei Ruan, Jiaqi Yang, Yuan Zhou
Motivated by practical needs such as large-scale learning, we study the impact of adaptivity constraints to linear contextual bandits, a central problem in online active learning.…
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
Zihan Zhang, Yuan Zhou, Xiangyang Ji
In this paper we consider the problem of learning an -optimal policy for a discounted Markov Decision Process (MDP). Given an MDP with states, actions, the discount fact…
Adaptive Double-Exploration Tradeoff for Outlier Detection
Xiaojin Zhang, Honglei Zhuang, Shengyu Zhang +1
We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold…
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition
Zihan Zhang, Yuan Zhou, Xiangyang Ji
We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with states, actions, and episode length . We prop…
Collaborative Top Distribution Identifications with Limited Interaction
Nikolai Karpov, Qin Zhang, Yuan Zhou
We consider the following problem in this paper: given a set of distributions, find the top- ones with the largest means. This problem is also called {\em top- arm identi…
Stochastically Differentiable Probabilistic Programs
David Tolpin, Yuan Zhou, Hongseok Yang
Probabilistic programs with mixed support (both continuous and discrete latent random variables) commonly appear in many probabilistic programming systems (PPSs). However, the exis…
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Yuan Zhou, Hongseok Yang, Yee Whye Teh +1
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing in…
-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
Kefan Dong, Jian Peng, Yining Wang +1
In this paper, we consider the problem of online learning of Markov decision processes (MDPs) with very large state spaces. Under the assumptions of realizable function approximati…
Exploration via Hindsight Goal Generation
Zhizhou Ren, Kefan Dong, Yuan Zhou +2
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a func…