Energy
Neur2SP: Neural Two-Stage Stochastic Programming
Having a mixed-integer linear program (MIP) or a nonlinear program (NLP) in the second stage further aggravates the intractability, even when specialized algorithms that exploit problem structure are employed.
Information-theoretic Task Selection for Meta-Reinforcement Learning
A common framework consists in modeling the range of tasks the agent may encounter as a distribution over all possible tasks.
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs Ben Norman
The objective is to maximize the total reward accumulated over all episodes (e.g., the number of games won), expressed as
94ab7b23a345f93333eac8748a66c763-Supplemental-Datasets_and_Benchmarks.pdf
This work is open-source under Open Data Commons Open Database License v1.0.
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
Molecular geometry prediction of flexible molecules, or conformer search, is a longstanding challenge in computational chemistry.
Reinforcement Learning with Augmented Data
Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL).
