143 citations · 308 across the 55 of their papers we have counts for
4 papers · 1 filter
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Leo Feng, Padideh Nouri, Aneri Muni +2
The ability to accelerate the design of biological sequences can have a substantial impact on the progress of the medical field. The problem can be framed as a global optimization…
The Primacy Bias in Deep Reinforcement Learning
Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro +2
This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of…
Continuous-Time Meta-Learning with Forward Mode Differentiation
Tristan Deleu, David Kanaa, Leo Feng +4
Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm wh…
Myriad: a real-world testbed to bridge trajectory optimization and deep learning
Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar +1
We present Myriad, a testbed written in JAX for learning and planning in real-world continuous environments. The primary contributions of Myriad are threefold. First, Myriad provid…