Willem Röpke
@willemropke.bsky.social
PhD student | Interested in all things decision-making and learning
5/ This was a collaborative effort across multiple universities that began over a year ago. A huge thanks to my co-authors for seeing it through with me and everyone who shared valuable insights along the way.
If you're interested in our work, I'd love to hear from you!
If you're interested in our work, I'd love to hear from you!
February 17, 2025 at 1:22 PM
5/ This was a collaborative effort across multiple universities that began over a year ago. A huge thanks to my co-authors for seeing it through with me and everyone who shared valuable insights along the way.
If you're interested in our work, I'd love to hear from you!
If you're interested in our work, I'd love to hear from you!
4/ Beyond RL, IPRO has applications in other domains like multi-objective path planning, which we’ve recently added support for to the codebase! If you work on decision-making under trade-offs, this might be relevant to you.
February 17, 2025 at 1:22 PM
4/ Beyond RL, IPRO has applications in other domains like multi-objective path planning, which we’ve recently added support for to the codebase! If you work on decision-making under trade-offs, this might be relevant to you.
3/ By incorporating oracles with theoretical guarantees, we can leverage these for the multi-objective problem. At the same time, we can adapt strong RL algorithms such as DQN, A2C, and PPO, making IPRO both practical and theoretically sound.
February 17, 2025 at 1:22 PM
3/ By incorporating oracles with theoretical guarantees, we can leverage these for the multi-objective problem. At the same time, we can adapt strong RL algorithms such as DQN, A2C, and PPO, making IPRO both practical and theoretically sound.
2/ IPRO decomposes the multi-objective problem into a sequence of single-objective problems. By solving each step efficiently, it systematically explores the search space while keeping track of what remains.
February 17, 2025 at 1:22 PM
2/ IPRO decomposes the multi-objective problem into a sequence of single-objective problems. By solving each step efficiently, it systematically explores the search space while keeping track of what remains.
1/ In many real-world problems, agents must balance multiple conflicting objectives—think of self-driving cars optimising speed vs. safety or AI assistants trading off response quality vs. efficiency.
How can we design efficient RL algorithms for such settings?
How can we design efficient RL algorithms for such settings?
February 17, 2025 at 1:22 PM
1/ In many real-world problems, agents must balance multiple conflicting objectives—think of self-driving cars optimising speed vs. safety or AI assistants trading off response quality vs. efficiency.
How can we design efficient RL algorithms for such settings?
How can we design efficient RL algorithms for such settings?