The SDM & RL core technology cluster provides a network for researchers working on sequential decision-making problems.

We, the SDM & RL cluster, focus on exploring the fascinating field of sequential decision-making. We are interested in developing algorithms and models that enable intelligent agents to make optimal decisions in dynamic and uncertain environments. Most of us research in the direction of applied or foundational (deep) reinforcement learning, while another large subset of us works with robotics and control theory.

Goal: The main purpose of the cluster is to provide a network for practitioners and researchers in the SDM & RL field, to advance our understanding of the fundamental principles underlying intelligent, sequential decision-making, to foster scientific discussion and collaboration, and to exchange practical knowledge.

Join the Cluster

The cluster uses the public Slack channel:

#ctc_sequential_decision_making_and_reinforcement_learning

To get started, simply fill out the cluster registration form and join us on Slack!

Cluster Activities

With respect to the goal of the cluster, we meet bi-weekly via zoom and typically engage in one of the following activities:

    • Research discussions: Cluster members are encouraged to present their research in SDM & RL, engaging in valuable scientific discourse.
    • Paper discussions: The cluster discusses SOTA papers from the field. Typically this involves one person presenting a summary of the paper, followed by a discussion.
    • Practical knowledge exchange: The cluster discusses implementational details, hyperparameters, and heuristics associated with the complex code underlying sequential decision-making problems and algorithms.
    • External speaker invitation: The cluster occasionally invites external (potential industrial) speakers to talk about their SDM & RL research.

Recent Publications from Cluster Members

A selection of the latest research published by members of our cluster:

Active Cluster Members

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Affiliation: Chalmers University of Technology

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Affiliation: KTH

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Affiliation: Lund University

Keywords: DRL, Multi-objective RL, Knowledge Transfer, Explainability
Affiliation: Örebro University

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Affiliation: Luleå University

Keywords: Autonomous drug design, bandits, RL, active learning/online learning,
Affiliation: Chalmers University of Technology

Keywords: Vision, Pose Estimation, Robotics, RL, Sim2Real
Affiliation: Lund University

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Affiliation: Chalmers University of Technology

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Affiliation: Uppsala University
Company: AstraZeneca

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Affiliation: Linköping University

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Affiliation: Chalmers University of Technology
Company: SAAB

Keywords: Curiosity driven exploration
Affiliation: Örebro University
Company: Nexer

Keywords: DRL, RL in games, generalization, scalability
Affiliation: KTH

 

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Affiliation: KTH

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Affiliation: KTH

Keywords: Human motion prediction, dynamics mapping
Affiliation: Örebro University

Current Cluster Leader

Stefan Stojanovic

PhD student, Division of Decision and Control Systems, KTH