7 citations · 10 across the 5 of their papers we have counts for
6 papers
Lexicographic Multi-Objective On-Policy Distillation
Doseok Jang, Jon Ander Campos, Youran Qi
Reinforcement learning from verifiable rewards (RLVR) usually optimizes answer correctness, yet useful language-model behavior also requires high-quality reasoning and concise resp…
Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning
Vincent Taboga, Justin Veilleux, Doseok Jang +2
Reinforcement learning (RL) has achieved strong results in control, yet learned policies remain brittle to changes in dynamics, action spaces, observation spaces, or goals, a criti…
Active Reinforcement Learning for Robust Building Control
Doseok Jang, Larry Yan, Lucas Spangher +1
Reinforcement learning (RL) is a powerful tool for optimal control that has found great success in Atari games, the game of Go, robotic control, and building optimization. RL is al…
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning
Doseok Jang, Larry Yan, Lucas Spangher +1
Multi-Agent Reinforcement Learning currently focuses on implementations where all data and training can be centralized to one machine. But what if local agents are split across mul…
Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs
Doseok Jang, Lucas Spangher, Manan Khattar +3
Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community,…
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response
Doseok Jang, Lucas Spangher, Manan Khattar +2
Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community,…