Kaiqing Zhang
Assistant Professor
Studies reinforcement learning, multi-agent learning and game theory for control and robotics.
Kaiqing Zhang's lab at UMD works on reasoning & neuro-symbolic AI, reinforcement learning and planning, control & multi-robot systems. 18 papers since 2023.
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 0%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 5 papers2026 · T1 0 · collecting
- 2 papers2025 · T1 0
- 4 papers2024 · T1 0
- 7 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Reinforcement learning11 papers
- Planning, control & multi-robot systems7 papers
- Reasoning & neuro-symbolic AI0 papers
- multi-agent learning0 papers
- studies reinforcement0 papers
Paper list
From the lab homepage.
20265 papers
- IEEE Conf. on Decision and Control (CDC)Joint communication-control strategy optimization with partially nested information structures: The linear-quadratic case (opens in a new tab)
- Intl. Conf. on Learning Represent. (ICLR) (Oral)Online learning and equilibrium computation with ranking feedback (opens in a new tab)
- SIAM Journal on Control and Optimization (SICON)Partially observable multi-agent reinforcement learning with information sharing (opens in a new tab)
- Intl. Conf. on Machine Learning (ICML), .Post-training LLMs as better decision-making agents: A regret-minimization approach (opens in a new tab)
- Conf. on Learning Theory (COLT)Regret minimization with adaptive opponents in repeated games (opens in a new tab)
20252 papers
- Journal of Machine Learning Research (JMLR)Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs (opens in a new tab)
- Intl. Conf. on Learning Represent. (ICLR), .Do LLM agents have regret? A case study in online learning and games (opens in a new tab)
20244 papers
- Management Science (MS)Model-free non-stationary RL: Near-optimal regret and applications in multi-agent RL and inventory control (opens in a new tab)
- Neural Info. Process. Systems (NeurIPS)Provable partially observable reinforcement learning with privileged information (opens in a new tab)
- Intl. Conf. on Learning Represent. (ICLR)Robot fleet learning via policy merging (opens in a new tab)
- ACM Conf. on Econ. and Comput. (EC)Two-timescale Q-learning with function approximation in zero-sum stochastic games (opens in a new tab)
20237 papers
- Conf. on Learning Theory (COLT)Breaking the curse of multiagents in a large state space: RL in Markov games with independent linear function approximation (opens in a new tab)
- Learning for Dynamics & Control (L4DC) (Oral)Can direct latent model learning solve linear quadratic Gaussian control? (opens in a new tab)
- Intl. Conf. on Learning Represent. (ICLR)Does learning from decentralized non-IID unlabeled data benefit from self supervision? (opens in a new tab)
- Journal of Machine Learning Research (JMLR) (Short version appeared at NeurIPS (Spotlight))Model-based multi-agent RL in zero-sum Markov games with near-optimal sample complexity (opens in a new tab)
- Neural Info. Process. Systems (NeurIPS)Multi-player zero-sum Markov games with networked separable interactions (opens in a new tab)
- Conf. on Learning Theory (COLT)Tackling combinatorial distribution shift: A matrix completion perspective (opens in a new tab)
- Conf. on Learning Theory (COLT)The complexity of Markov equilibrium in stochastic games (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UMDFall 2027
Not announced yet
UMD Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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Data
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