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Welcome to the Reinforcement Learning Lab (RLLab) at KAIST. We study various RL settings motivated by real-world problems and design algorithms with provable guarantees.
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I am looking to hire self-motivated PhD students with a strong background in mathematics who enjoy writing rigorous proofs to work on the design and analysis of reinforcement learning algorithms across a variety of settings.
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Event-based reinforcement learning focuses on environments where actions are triggered by events rather than at fixed time intervals. This approach is particularly relevant for applications such as healthcare, where patient events occur irregularly, or in resource allocation problems with event-triggered decision points. Our research develops algorithms that can efficiently learn optimal policies in these challenging event-driven settings.
In safety-critical applications, it is essential to restrict policies to those that satisfy safety constraints. Safe reinforcement learning algorithms aim to identify the optimal policy within this restricted set. For example, in autonomous driving, an RL agent may learn to navigate efficiently, but it must also guarantee that safety distances are maintained and traffic rules are never violated. Our research focuses on developing statistically and computationally efficient algorithms for safety-critical reinforcement learning applications.
In many applications, direct interaction with the environment is costly or risky. In such cases, it is essential to leverage previously collected offline data—whether gathered from safe but suboptimal human decisions or from high-fidelity simulators. Our work develops algorithms that are both statistically and computationally efficient, enabling reliable learning in these offline settings.