Sequential Decision-Making and Reinforcement Learning

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:

• Enhancing pre-trained decision transformers with prompt-tuning bandits
  Finn Rietz, Sara Karimi et al. (2025)
• A graph-based reinforcement learning approach with frontier potential based reward for safe cluttered environment exploration
  Gabriele Calzolari et al. (2025)
• Efficient prior selection in Gaussian process bandits with Thompson sampling
  Jack Sandberg et al. (2025)
• Automatic planning and optimization of a laser radar inspection system
  Jack Sandberg et al. (2025)
• Identifying 14-3-3 interactome binding sites with deep learning
  Laura van Weesep et al. (2025)
• Platoon coordination and leader selection in mixed transportation systems via dynamic programming
  Ying Wang et al. (2025)
• Fast online learning of CLiFF-maps in changing environments
  Yufei Zhu et al. (2025)
• Learning to ground existentially quantified goals
  Martin Funkquist et al. (2024)
• SCORE: skill-conditioned online reinforcement learning
  Sara Karimi et al. (2024)
• Model-free low-rank reinforcement learning via leveraged entry-wise matrix estimation
  Stefan Stojanovic et al. (2024)
• Identifiable latent bandits: Combining observational data and exploration for personalized healthcare
  Ahmet Balcioglu et al. (2024)
• Multi-agent obstacle avoidance using velocity obstacles and control barrier functions
  Alejandro Sánchez Roncero et al. (2024)
• Diversity-aware reinforcement learning for de novo drug design
  Hampus Gummesson Svensson et al. (2024)
• Towards interpretable reinforcement learning with constrained normalizing flow policies
  Finn Rietz et al. (2024)
• Decentralized multi-agent reinforcement learning exploration with inter-agent communication-based action space
  Gabriele Calzolari et al. (2024)