Miao Liu

发表

Learning to Teach in Cooperative Multiagent Reinforcement Learning

Jonathan P. How, Gerald Tesauro, Miao Liu, 2018, AAAI.

Online Expectation Maximization for Reinforcement Learning in POMDPs

Lawrence Carin, Miao Liu, Xuejun Liao, 2013, IJCAI.

Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning

Jonathan P. How, Miao Liu, Yu Fan Chen, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Socially aware motion planning with deep reinforcement learning

Jonathan P. How, Miao Liu, Yu Fan Chen, 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

Jakob N. Foerster, G. Tesauro, J. How, 2022, ArXiv.

Context-Specific Representation Abstraction for Deep Option Learning

G. Tesauro, J. How, M. Riemer, 2021, AAAI.

ING OF DEEP NEURAL NETWORKS

Danlu Chen, Miao Liu, Jie Fu‘̂, 2016 .

Off-policy reinforcement learning with Gaussian processes Citation

Thomas J. Walsh, J. How, Miao Liu, 2014 .

Transfer Learning for Reinforcement Learning with Dependent Dirichlet Process and Gaussian Process

L. Carin, J. How, Miao Liu, 2012 .

InformationValue inNonparametricDirichlet-Process Gaussian-Process ( DPGP ) MixtureModels

J. How, Robert H. Klein, Shayegan Omidshafiei, 2016 .

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

Jakob N. Foerster, G. Tesauro, J. How, 2022 .

AI Planning Annotation in Reinforcement Learning: Options and Beyond

Don Joven Agravante, G. Tesauro, Michael Katz, 2021 .

Augmented dictionary learning for motion prediction

Yu Fan Chen, J. How, Miao Liu, 2016, IEEE International Conference on Robotics and Automation.

Influencing Long-Term Behavior in Multiagent Reinforcement Learning

G. Tesauro, J. How, M. Riemer, 2022, NeurIPS.

Learning Multi-Objective Curricula for Robotic Policy Learning

C. Pal, Miao Liu, Jie Fu, 2021, CoRL.

Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria

G. Tesauro, J. How, M. Riemer, 2022, ArXiv.

Augmented dictionary learning for motion prediction

Jonathan P. How, Miao Liu, Yu Fan Chen, 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Learning Hierarchical Teaching Policies for Cooperative Agents

G. Tesauro, J. How, Murray Campbell, 2019, AAMAS.

Multi-agent coordination method based on fuzzy Q-learning

Min Wu, Kuo-Chi Lin, Jun Peng, 2008, 2008 7th World Congress on Intelligent Control and Automation.

Efficient Bayesian Nonparametric Methods for Model-Free Reinforcement Learning in Centralized and Decentralized Sequential Environments

Miao Liu, Miao Liu, 2014 .

Learning Multi-Objective Curricula for Deep Reinforcement Learning

Chris Pal, Miao Liu, Abhinav Gupta, 2021, ArXiv.

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference pdf

Gerald Tesauro, Miao Liu, Irina Rish, 2018, ICLR.

Gaussian Processes for Learning and Control: A Tutorial with Examples

Jonathan P. How, Girish Chowdhary, Miao Liu, 2018, IEEE Control Systems.

Consensus of Two Leader-Following Multi-agent Systems between the Cooperation Network and the Competition Network

Miao Liu, Miao Liu, 2017, J. Comput..

MIT Open Access Articles Motion planning with diffusion maps

J. How, Miao Liu, Shih‐Yuan Liu, 2022 .

Motion planning with diffusion maps

Jonathan P. How, Miao Liu, Yu Fan Chen, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Learning Multi-Objective Curricula for Deep Reinforcement Learning pdf

Miao Liu, Chris Pal, Abhinav Gupta, 2021, ArXiv.

FORGETTING BY MAXIMIZING TRANSFER AND MINIMIZING INTERFERENCE

G. Tesauro, Y. Tu, Ignacio Cases, 2018 .

Predictive Modeling of Pedestrian Motion Patterns with Bayesian Nonparametrics

Jonathan P. How, Yu Fan Chen, Miao Liu, 2016 .

Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions

Jonathan P. How, Miao Liu, Christopher Amato, 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

The Infinite Regionalized Policy Representation

Lawrence Carin, Miao Liu, Xuejun Liao, 2011, ICML.

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning pdf

Gerald Tesauro, Jonathan P. How, Chuangchuang Sun, 2020, ICML.

Eigenoption Discovery through the Deep Successor Representation pdf

Marlos C. Machado, Xiaoxiao Guo, Gerald Tesauro, 2017, ICLR.

Learning Abstract Options

Gerald Tesauro, Miao Liu, Matthew Riemer, 2018, NeurIPS.

Hierarchical Reinforcement Learning with AI Planning Models

Don Joven Agravante, Geraud Nangue Tasse, Michael Katz, 2022, 2203.00669.

AI Planning Annotation for Sample Efficient Reinforcement Learning

Don Joven Agravante, G. Tesauro, Michael Katz, 2022, ArXiv.

Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations

Jonathan P. How, Miao Liu, Christopher Amato, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference

G. Tesauro, Y. Tu, Ignacio Cases, 2018, ICLR.

Model-free Causal Reinforcement Learning with Causal Diagrams

Miao Liu, Tian Gao, D. Bhattacharjya, 2023 .

Quickest change detection approach to optimal control in Markov decision processes with model changes

Jonathan P. How, Taposh Banerjee, Miao Liu, 2016, 2017 American Control Conference (ACC).

Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning pdf

Jonathan P. How, Gerald Tesauro, Miao Liu, 2019, ArXiv.

On the Role of Weight Sharing During Deep Option Learning

G. Tesauro, C. Rosenbaum, Ignacio Cases, 2019, AAAI.

The Eigenoption-Critic Framework pdf

Marlos C. Machado, Gerald Tesauro, Miao Liu, 2017, ArXiv.

Coagent Networks Revisited

Modjtaba Shokrian Zini, M. Riemer, Miao Liu, 2020, 2001.10474.

Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments

Jonathan P. How, Miao Liu, Christopher Amato, 2016, AAAI.

Scalable accelerated decentralized multi-robot policy search in continuous observation spaces

Jonathan P. How, Miao Liu, Christopher Amato, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Off-policy reinforcement learning with Gaussian processes

Girish Chowdhary, Thomas J. Walsh, Robert C. Grande, 2014, IEEE/CAA Journal of Automatica Sinica.

Deep Reinforcement Learning for Accelerating the Convergence Rate

Tat-Seng Chua, Jiashi Feng, Miao Liu, 2016 .

Deep Q-Networks for Accelerating the Training of Deep Neural Networks pdf

Tat-Seng Chua, Jiashi Feng, Jie Fu, 2016, ArXiv.

Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi

Miao Liu, Sarath Chandar, Janarthanan Rajendran, 2023, ArXiv.

Cost-Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment

Miao Liu, Yada Zhu, Di Chen, 2022, ICAIF.

Local Explanations for Reinforcement Learning

Miao Liu, Amit Dhurandhar, Ronny Luss, 2022, AAAI.

Stick-Breaking Policy Learning in Dec-POMDPs

Jonathan P. How, Lawrence Carin, Miao Liu, 2015, IJCAI.

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach

Miao Liu, K. Murugesan, Han Shen, 2023, ICLR.

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games pdf

Gerald Tesauro, Miao Liu, Chris R. Sims, 2020, ArXiv.

Automatic Pan–Tilt Camera Control for Learning Dirichlet Process Gaussian Process (DPGP) Mixture Models of Multiple Moving Targets

Jonathan P. How, Miao Liu, Silvia Ferrari, 2019, IEEE Transactions on Automatic Control.