Guannan Qu
Associate Professor
Studies reinforcement learning, control and optimization for networked and multi-agent systems.
Guannan Qu's lab at CMU works on reinforcement learning, learning theory & optimization and planning, control & multi-robot systems. 36 papers since 2023, 21 at top venues, mostly NeurIPS and ICLR. 7 PhD students and 2 master's students. 8 alumni. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 58%
- PhD students
- 7
- MS students
- 2
- alumni listed
- 8
Papers by yearfrom the lab homepage, 2023–2026
- 13 papers2026 · T1 11 · collecting
- 8 papers2025 · T1 7
- 7 papers2024 · T1 3
- 8 papers2023 · T1 0
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, L4DC, TMLR
Research focuspaper titles that use each topic's words
- Planning, control & multi-robot systems22 papers
- Learning theory & optimization14 papers
- Reinforcement learning13 papers
- networked7 papers
- studies reinforcement0 papers
Paper list
From the lab homepage.
202613 papers
- NeurIPS (T1)Activation-Keyed Momentum: An Anisotropic Momentum Update via the Delta Rule (opens in a new tab)
- ICML (T1)BLOCK-EM: Preventing Emergent Misalignment via Latent Blocking (opens in a new tab)
- TAC (T1)Distributed Truncated Predictive Control for Networked Systems under Uncertainty: Stability and Near-Optimality Guarantee (opens in a new tab)
- NeurIPS (T1)Emergent Misalignment as Data-Mediated Transfer (opens in a new tab)
- TMLR (T1)Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing (opens in a new tab)
- ICLR (T1)Internal Planning in Language Models: Characterizing Horizon and Branch Awareness (opens in a new tab)
- NeurIPS (T1)Learning Fractional-Order Dynamics from a Single Trajectory (opens in a new tab)
- ICLR (T1)Much Ado About Noising: Dispelling the Myths of Generative Robotic Control (opens in a new tab)
- TMLR (T1)Natural Policy Gradient for Average Reward Non-Stationary Reinforcement Learning (opens in a new tab)
- Applied EnergyPhysics-Informed RL for Real-World HVAC Controls: Lessons from Imitation to Deployment
- ICLR (T1)Polynomial Convergence of Riemannian Diffusion Models (opens in a new tab)
- European Control ConferenceTransformer-Based Multi-Agent Reinforcement Learning for Networked Systems with Long-Range Interactions (opens in a new tab)
20258 papers
- ICML (T1)A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization (opens in a new tab)
- RSS (T1)ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills (opens in a new tab)
- ACCFast Bandit-based Policy Adaptation in Diverse Environments (opens in a new tab)
- ICRA (T1)Full-order Sampling-Based MPC for Torque-Level Locomotion Control via Diffusion-Style Annealing (opens in a new tab)
- UAI (T1)Learning to Stabilize Unknown LTI Systems on a Single Trajectory under Stochastic Noise (opens in a new tab)
- NeurIPS (T1)Mean-field Sampling for Cooperative Multi-Agent Reinforcement Learning (opens in a new tab)
- TMLR (T1)Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information (opens in a new tab)
- NeurIPS (T1)Stabilizing LTI Systems under Partial Observability: Sample Complexity and Fundamental Limits (opens in a new tab)
20247 papers
- L4DCCombining Model-based Controller and ML Advice via Convex Reparameterization (opens in a new tab)
- L4DCCoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design (opens in a new tab)
- NeurocomputingDecentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines (opens in a new tab)
- AISTATS (T1)Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems (opens in a new tab)
- ICML (T1)Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies (opens in a new tab)
- NeurIPS (T1)Model-based Diffusion for Trajectory Optimization (opens in a new tab)
- ACCStability and Regret Bounds on Distributed Truncated Predictive Control for Networked Dynamical Systems (opens in a new tab)
20238 papers
- —A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control (opens in a new tab)
- IEEE Open Journal of Control SystemsCertifying Black-Box Policies with Stability for Nonlinear Control (opens in a new tab)
- L4DCCompositional Neural Certificates for Networked Dynamical Systems (opens in a new tab)
- ACM SIGMETRICSGlobal Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning (opens in a new tab)
- CDCKCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Discrete-Time Systems (opens in a new tab)
- CDCNatural Policy Gradient Preserves Spatial Decay Properties for Control of Networked Dynamical Systems (opens in a new tab)
- SIAM Journal on Control and OptimizationNear-Optimal Distributed Linear-Quadratic Regulator for Networked Systems (opens in a new tab)
- IEEE Transactions on Control of Network SystemsStability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control (opens in a new tab)
Where alumni went8 listed, 0% to industry
Industry 0Academia 8
Also to
- Caltech
- MIT
- Carnegie Mellon University
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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