Distributed learning

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Our learning direction focuses on reinforcement learning (RL) and meta-learning for decision-making and adaptive control, with an emphasis on networked and multi-agent systems.

What this field is about

• Reinforcement learning: learn a policy π(a|s) by interacting with an environment and maximizing long-term return.
• RL for control: data-driven controllers for tracking, stabilization, and planning under uncertainty and constraints.
• Multi-agent / networked RL: coordination and communication among agents (e.g., formation, coverage, and distributed decision-making).
• Meta-learning: learn a good initialization or update rule so a policy/controller adapts to a new task with only a few samples or gradient steps.
• Key themes: safety & stability, sample efficiency, and generalization across tasks and operating conditions.

Illustrations

Reinforcement learning (RL)

Agent–environment interaction, policy improvement, and a typical learning curve (episode return).

Meta-learning

Meta-training across tasks to obtain an initialization that adapts quickly to a new task (few-shot).

Typical applications

• Learning-based control for robotics and autonomous vehicles (navigation, tracking, manipulation).
• Adaptive decision-making in networked systems (smart grids, smart manufacturing, traffic and mobility).
• Multi-agent coordination: formation, coverage, and task allocation with learned policies.
• Rapid adaptation to changing dynamics or environments via meta-learning (few-shot personalization).

Related reading

• Sutton & Barto. Reinforcement Learning: An Introduction (2nd ed.).
• Finn, Abbeel & Levine. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML), 2017.
• Schulman et al. Proximal Policy Optimization Algorithms (PPO), 2017.
• Haarnoja et al. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning, 2018.
• Duan et al. RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning, 2016.
• Hu Wenbo; Fei Chen*; et al. Multi-ASV coordinated tracking via model-reference reinforcement learning control (IEEE TCYB, 2023).
• Fei Chen; Wei Ren. On the Control of Multi-Agent Systems: A Survey (FnT Systems and Control, 2019).
• Multi-agent reinforcement learning survey (select one as a starting point).

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