Daoyi Dong

About

Daoyi Dong is a prominent researcher whose work sits at the intersection of reinforcement learning, quantum-inspired computing, and autonomous robotics. Over more than fifteen years of sustained inquiry, he has made foundational contributions to intelligent robot navigation, pioneering quantum-inspired reinforcement learning (QiRL) algorithms that harness principles such as quantum superposition and probabilistic action selection to dramatically improve learning efficiency in autonomous systems. His 2010 paper on robust QiRL for robot navigation has garnered 97 citations, reflecting its enduring influence on the field, while his earlier 2008 hierarchical Q-learning framework — with 70 citations — established important groundwork for hybrid control architectures in mobile robotics. Dong's research has consistently pushed into challenging real-world scenarios, addressing dynamic and lifelong learning environments through novel incremental reinforcement learning methods and Dirichlet process mixture models designed to combat catastrophic forgetting. His contributions extend further to multi-robot coordination, distributed sampled-data control, and rule-based navigation strategies that reduce computational complexity for deployment in complex settings. More recently, he has explored evolution strategies as scalable alternatives to traditional RL approaches. Collectively, his body of work — spanning quantum mechanics, adaptive learning, and robotic autonomy — offers students and researchers a rich, forward-thinking roadmap for building more intelligent, resilient autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Robust Quantum-Inspired Reinforcement Learning for Robot Navigation

   97 citations · 2010
2. 2
   Incremental Reinforcement Learning With Prioritized Sweeping for Dynamic Environments

   70 citations · 2019
3. 3
   Hybrid Control for Robot Navigation - A Hierarchical Q-Learning Algorithm

   70 citations · 2008
4. 4
   Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks

   53 citations · 2017
5. 5
   Rule-Based Reinforcement Learning for Efficient Robot Navigation With Space Reduction

   52 citations · 2021
6. 6
   Quantum Mechanics Helps in Learning for More Intelligent Robots

   23 citations · 2006
7. 7
   A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning

   22 citations · 2022
8. 8
   Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments

   21 citations · 2022
9. 9
   Hybrid MDP based integrated hierarchical Q-learning

   20 citations · 2011
10. 10
    A Quantum-inspired Q-learning Algorithm for Indoor Robot Navigation

    12 citations · 2008

Key Collaborators

Contact & Links