Stefano V. Albrecht
Associate Professor
Works on autonomous agents, multi-agent interaction and reinforcement learning.
Stefano V. Albrecht's lab at NTU works on reinforcement learning, reasoning & neuro-symbolic AI and embodied & active vision. 49 papers since 2023, 21 at top venues, mostly TMLR and ICLR. 8 PhD students. 16 alumni.
At a glance
- papers, 2023–2026
- 13/ year
- at T1 venues
- 43%
- PhD students
- 8
- MS students
- 0
- alumni listed
- 16
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 2 · collecting
- 10 papers2025 · T1 4
- 15 papers2024 · T1 8
- 15 papers2023 · T1 7
T1 venuesOtherVenues: TMLR, ICLR, NeurIPS, ICRA, IROS
Research focuspaper titles that use each topic's words
- Reinforcement learning28 papers
- multi-agent14 papers
- Reasoning & neuro-symbolic AI0 papers
- Embodied & active vision0 papers
- autonomous agents0 papers
Paper list
From the lab homepage.
20269 papers
- AAAI-26 Workshop on LLM-based Multi-Agent Systems: Towards Responsible, Reliable, and Scalable Agentic SystemsARCANE: A Multi-Agent Framework for Interpretable and Configurable Alignment
- Reinforcement Learning ConferenceAssistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
- Cambridge University PressBecoming an AI Researcher: Practical Advice for Graduate Students
- International Conference on Autonomous Agents and Multi-Agent SystemsFairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas
- Science Robotics (T1)From Autonomy to Alliance: Robotic Foundation Models Must Learn With Us, Not Just For Us
- International Conference on Autonomous Agents and Multi-Agent SystemsIntegrating Counterfactual Simulations with Language Models for Explaining Multi-Agent Behaviour
- TMLR (T1)NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria
- AAAI-26 Workshop on LLM-based Multi-Agent Systems: Towards Responsible, Reliable, and Scalable Agentic SystemsSTAR-MARL: LLM-based Sub-task Curricula Design
- AAAI-26 Workshop on LLM-based Multi-Agent Systems: Towards Responsible, Reliable, and Scalable Agentic SystemsTowards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective
202510 papers
- RLC Workshop on Coordination and Cooperation in Multi-Agent Reinforcement LearningAssistax: A Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
- International Conference on Autonomous Agents and Multi-Agent SystemsEnsemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
- Accident Analysis & Prevention (Journal)HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation
- TMLR (T1)Highway Graph to Accelerate Reinforcement Learning
- NeurIPS (T1)HyperMARL: Adaptive Hypernetworks for Multi-Agent RL
- ICLR (T1)Online-to-Offline RL for Agent Alignment
- International Conference on Distributed Artificial IntelligenceOrchestrating Human-AI Teams: The Manager Agent as a Unifying Research Challenge
- Conference on Human Factors in Computing SystemsPeople Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI
- ICLR (T1)Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
- NeurocomputingUsing Offline Data to Speed-up Reinforcement Learning in Procedurally Generated Environments
202415 papers
- RLC Workshop on Coordination and Cooperation for Multi-Agent Reinforcement Learning MethodsAgent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
- International Conference on Autonomous Agents and Multi-Agent SystemsCausal Explanations for Sequential Decision-Making in Multi-Agent Systems
- AAAI (T1)Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
- ICRA (T1)DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
- ICML (T1)DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design
- IEEE Transactions on Intelligent Transportation SystemsExplainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
- International Conference on Autonomous Agents and Multi-Agent SystemsLearning Complex Teamwork Tasks Using a Given Sub-task Decomposition
- COLING (T1)LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots
- ICLR (T1)lpNTK: Better Generalisation with Less Data via Sample Interaction During Learning
- MIT PressMulti-Agent Reinforcement Learning: Foundations and Modern Approaches
- TMLR (T1)Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning
- Reinforcement Learning ConferenceMulti-view Disentanglement for Reinforcement Learning with Multiple Cameras
202315 papers
- JMLR (T1)A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
- AAMAS WorkshopCausal Social Explanations for Stochastic Sequential Multi-Agent Decision-Making
- NeurIPS (T1)Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
- TMLR (T1)Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity
- NeurIPS WorkshopHow the level sampling process impacts zero-shot generalisation in deep reinforcement learning
- NeurIPS WorkshopIs Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement Learning
- AAMAS WorkshopLearning Complex Teamwork Tasks Using a Sub-task Curriculum
- NeurIPS WorkshopLearning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
- RSS WorkshopMIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments
- TMLR (T1)Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
- ICRA (T1)Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
- AAMAS WorkshopRevisiting the Gumbel-Softmax in MADDPG
Where alumni went16 listed
Apply to NTUFall 2027
Not announced yet
Computing and Data Science PhD
- Round
- Main
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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