Ben Eysenbach
@ben-eysenbach.bsky.social
Assistant professor at Princeton CS working on reinforcement learning and AI/ML.
Site: https://ben-eysenbach.github.io/
Lab: https://princeton-rl.github.io/
Site: https://ben-eysenbach.github.io/
Lab: https://princeton-rl.github.io/
New research directions:
* model-based RL with NF models,
* goal/language-conditioned NF foundation policies,
* NFs for collocation-based planning,
* goal-conditioned NF value functions (as control barrier functions, as Lyapunov functions).
👆Join/scoop us -- we can't do it all!
* model-based RL with NF models,
* goal/language-conditioned NF foundation policies,
* NFs for collocation-based planning,
* goal-conditioned NF value functions (as control barrier functions, as Lyapunov functions).
👆Join/scoop us -- we can't do it all!
June 5, 2025 at 6:21 PM
New research directions:
* model-based RL with NF models,
* goal/language-conditioned NF foundation policies,
* NFs for collocation-based planning,
* goal-conditioned NF value functions (as control barrier functions, as Lyapunov functions).
👆Join/scoop us -- we can't do it all!
* model-based RL with NF models,
* goal/language-conditioned NF foundation policies,
* NFs for collocation-based planning,
* goal-conditioned NF value functions (as control barrier functions, as Lyapunov functions).
👆Join/scoop us -- we can't do it all!
2/ Much of my past research is about avoiding density estimation in RL, because I've assumed that it's difficult and fickle. But, if NFs make it easy to do high-dim density estimation, there are lots of new RL algorithms to be developed:
June 5, 2025 at 6:21 PM
2/ Much of my past research is about avoiding density estimation in RL, because I've assumed that it's difficult and fickle. But, if NFs make it easy to do high-dim density estimation, there are lots of new RL algorithms to be developed:
While we still don't understand precisely why depth helps so much, the benefits seem correlated with exploration. Thought experiment: What if the answer to the exploration problem in RL were to just increase network depth?
March 21, 2025 at 4:17 PM
While we still don't understand precisely why depth helps so much, the benefits seem correlated with exploration. Thought experiment: What if the answer to the exploration problem in RL were to just increase network depth?