ΑΙhub.org
 

Conference on Reinforcement Learning and Decision Making


by
05 July 2022



share this:

The 5th Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 2022 took place at Brown University from 8-11 June. The programme included invited and contributed talks, workshops, and poster sessions. The goal of RLDM is to provide a platform for communication among all researchers interested in learning and decision making over time to achieve a goal.

Over the last few decades, reinforcement learning and decision making have been the focus of an incredible wealth of research spanning a wide variety of fields including psychology, artificial intelligence, machine learning, operations research, control theory, neuroscience, economics and ethology. The interdisciplinary sharing of ideas has been key to many developments in the field, and the meeting is characterized by the multidisciplinarity of the presenters and attendees.

Michael Littman (one of the conference general chairs) said that the conference had been a great success, both in terms of the organization and the content: “For many of us, it was the first in-person conference since the start of the pandemic. The organizers put a lot of thought into ways of keeping people safe from COVID and it appears to have paid off, with very few attendees testing positive. RLDM is always exciting, in part because of the effort to coordinate between the cognitive/neuroscience researchers studying decision-making in natural systems and the AI/ML researchers looking at decision-making in machines”.

RLDM lecture theatreOne of the speakers at RLDM. Photo credit: Michael J Frank.

Watch the recordings of the talks

The talks from the four days of the conference were recorded, and you can watch them here:
Day 1 | Day 2 | Day 3 | Day 4

The talks are also available split by individual speakers here.

Best paper awards

Two articles received the honour of RLDM 2022 Best Paper Award:

  • Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, Philip S. Thomas, Universal off-policy evaluation.
  • Diksha Gupta, Brian DePasquale, Charles Kopec, Carlos Brod, An explanatory link between history biases and lapses.

Some of the participants shared their experience on Twitter.

The event website is here.




Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.




            AIhub is supported by:


Related posts :



The Machine Ethics podcast: AI Ethics, Risks and Safety Conference 2025

Listen to a special episode recorded at the AI Ethics, Risks and Safety Conference.

Interview with Aneesh Komanduri: Causality and generative modeling

  31 Jul 2025
Read the latest interview in our series featuring the AAAI/SIGAI Doctoral Consortium participants.
monthly digest

AIhub monthly digest: July 2025 – RoboCup round-up, ICML in Vancouver, and leveraging feedback in human-robot interactions

  30 Jul 2025
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

Interview with Yuki Mitsufuji: Text-to-sound generation

  29 Jul 2025
We hear from Sony AI Lead Research Scientist Yuki Mitsufuji to find out more about his latest research.

Open-source Swiss language model to be released this summer

  29 Jul 2025
This summer, EPFL and ETH Zurich will release a large language model (LLM) developed on public infrastructure.

Interview with Kate Candon: Leveraging explicit and implicit feedback in human-robot interactions

  25 Jul 2025
Hear from PhD student Kate about her work on human-robot interactions.

#RoboCup2025: social media round-up part 2

  24 Jul 2025
Find out what participants got up to during the second half of RoboCup2025 in Salvador, Brazil.

Visualising the digital transformation of work

Does it matter that the existing images of AI and digital technologies are so unrealistic?



 

AIhub is supported by:






©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence