Diederik Roijers
@diederikro.bsky.social
--- Oh my, I'm in AI! ---
RL, MAS, and GT researcher @ Vrije Universiteit Brussel
Business Developer @ City of Amsterdam
RL, MAS, and GT researcher @ Vrije Universiteit Brussel
Business Developer @ City of Amsterdam
Finally! We created an anytime outer loop algorithm for identifying the whole Pareto front in any multi-objective problem. Awesome work, Willem!
Exciting news! My paper on multi-objective reinforcement learning was accepted at AAMAS 2025!
We introduce IPRO (Iterated Pareto Referent Optimisation)—a principled approach to solving multi-objective problems.
🔗 Paper: arxiv.org/abs/2402.07182
💻 Code: github.com/wilrop/ipro
We introduce IPRO (Iterated Pareto Referent Optimisation)—a principled approach to solving multi-objective problems.
🔗 Paper: arxiv.org/abs/2402.07182
💻 Code: github.com/wilrop/ipro
February 17, 2025 at 9:27 PM
Finally! We created an anytime outer loop algorithm for identifying the whole Pareto front in any multi-objective problem. Awesome work, Willem!
Reposted by Diederik Roijers
I collected some folk knowledge for RL and stuck them in my lecture slides a couple weeks back: web.mit.edu/6.7920/www/l... See Appendix B... sorry, I know, appendix of a lecture slide deck is not the best for discovery. Suggestions very welcome.
web.mit.edu
November 27, 2024 at 1:36 PM
I collected some folk knowledge for RL and stuck them in my lecture slides a couple weeks back: web.mit.edu/6.7920/www/l... See Appendix B... sorry, I know, appendix of a lecture slide deck is not the best for discovery. Suggestions very welcome.
Same for me, really happy with this work.
I'm going to post about a few of my key and/or recent papers to create some context for my bsky profile.
Probably the most important paper I've been part of: link.springer.com/article/10.1...
This practical guide reviews the 'why' and 'how' of multi-objective reinforcement learning.
Probably the most important paper I've been part of: link.springer.com/article/10.1...
This practical guide reviews the 'why' and 'how' of multi-objective reinforcement learning.
A practical guide to multi-objective reinforcement learning and planning - Autonomous Agents and Multi-Agent Systems
Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learnin...
link.springer.com
November 24, 2024 at 8:49 PM
Same for me, really happy with this work.