Chenjun Xiao

发表

Escaping the Gravitational Pull of Softmax

Csaba Szepesvári, Dale Schuurmans, Bo Dai, 2020, NeurIPS.

On the Global Convergence Rates of Softmax Policy Gradient Methods

Dale Schuurmans, Chenjun Xiao, Jincheng Mei, 2020, ICML.

Factorization Ranking Model for Fast Move Prediction in the Game of Go

Chenjun Xiao, 2016 .

An Exploration of Predictive Representations of State by Chen Ma

Kristopher De Asis, Chenjun Xiao, M. Schlegel, 2020 .

TERIZATION IN RECURSIVE VALUE ESTIMATION

D. Schuurmans, Chris Harris, Jincheng Mei, 2022 .

Exploration in Policy Mirror Descent

Dale Schuurmans, Martin Müller, Ruitong Huang, 2018 .

On the Global Convergence Rates of Softmax Policy Gradient Methods

Csaba Szepesvari, D. Schuurmans, Jincheng Mei, 2020, ICML.

Latent Variable Representation for Reinforcement Learning

D. Schuurmans, Sujay Sanghavi, Bo Dai, 2022, 2212.08765.

Latent Variable Representation for Reinforcement Learning

D. Schuurmans, Sujay Sanghavi, Bo Dai, 2022, ICLR.

Memory-Augmented Monte Carlo Tree Search

Martin Müller, Chenjun Xiao, Jincheng Mei, 2018, AAAI.

Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning

Yangchen Pan, Sarath Chandar, Chenjun Xiao, 2023, ArXiv.

Only-One-Victor Pattern Learning in Computer Go

I-Chen Wu, Jiao Wang, Wen-Jie Tseng, 2017, IEEE Transactions on Computational Intelligence and AI in Games.

On the Optimality of Batch Policy Optimization Algorithms

Tor Lattimore, Dale Schuurmans, Chenjun Xiao, 2021, ICML.

On Principled Entropy Exploration in Policy Optimization

Martin Müller, Dale Schuurmans, Ruitong Huang, 2019, IJCAI.

Learning to Combat Compounding-Error in Model-Based Reinforcement Learning pdf

Yifan Wu, Chen Ma, Dale Schuurmans, 2019, ArXiv.

Understanding and Leveraging Overparameterization in Recursive Value Estimation

D. Schuurmans, Chris Harris, Bo Dai, 2022, ICLR.

L ATENT V ARIABLE R EPRESENTATION FOR R EINFORCE - MENT L EARNING

D. Schuurmans, Sujay Sanghavi, Bo Dai, 2023 .

The Curse of Passive Data Collection in Batch Reinforcement Learning

Csaba Szepesvari, D. Schuurmans, Bo Dai, 2021, AISTATS.

The Curse of Passive Data Collection in Batch Reinforcement Learning

Csaba Szepesvari, D. Schuurmans, Bo Dai, 2021, 2106.09973.

Understanding the Effect of Stochasticity in Policy Optimization pdf

Dale Schuurmans, Jincheng Mei, Csaba Szepesvari, 2021, NeurIPS.

In-Sample Policy Iteration for Offline Reinforcement Learning

Chenjun Xiao, Zhaopeng Meng, Yi Ma, 2023, ArXiv.

The In-Sample Softmax for Offline Reinforcement Learning

Adam White, Martha White, Yangchen Pan, 2023, International Conference on Learning Representations.

Maximum Entropy Monte-Carlo Planning

Dale Schuurmans, Ruitong Huang, Chenjun Xiao, 2019, NeurIPS.

Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning

Yangchen Pan, Sarath Chandar, Chenjun Xiao, 2023, UAI.

The Curse of Passive Data Collection in Batch Reinforcement Learning

Csaba Szepesvari, D. Schuurmans, Bo Dai, 2022, AISTATS.

Iteratively Refined Behavior Regularization for Offline Reinforcement Learning

Chenjun Xiao, Zhaopeng Meng, Yi Ma, 2023, 2306.05726.

Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning

Yangchen Pan, Sarath Chandar, Chenjun Xiao, 2023, UAI.

Efficient Reinforcement Learning from Partial Observability

D. Schuurmans, Chenjun Xiao, Bo Dai, 2023, 2311.12244.

Energy-based Predictive Representations for Partially Observed Reinforcement Learning

D. Schuurmans, Joseph E. Gonzalez, Bo Dai, 2023, UAI.

Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning

D. Schuurmans, Chenjun Xiao, Bo Dai, 2023, ArXiv.