activity
20202024
most citedREPAINT: Knowledge Transfer in Deep Reinforcement Learning

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LO2024

Extended Resolution Clause Learning via Dual Implication Points

Sam Buss, Jonathan Chung, Vijay Ganesh +1

We present a new extended resolution clause learning (ERCL) algorithm, implemented as part of a conflict-driven clause-learning (CDCL) SAT solver, wherein new variables are dynamic…

cs.LO2024

A Reinforcement Learning based Reset Policy for CDCL SAT Solvers

Chunxiao Li, Charlie Liu, Jonathan Chung +3

Restart policy is an important technique used in modern Conflict-Driven Clause Learning (CDCL) solvers, wherein some parts of the solver state are erased at certain intervals durin…

cs.LO2021

On the Hierarchical Community Structure of Practical Boolean Formulas

Chunxiao Li, Jonathan Chung, Soham Mukherjee +5

Modern CDCL SAT solvers easily solve industrial instances containing tens of millions of variables and clauses, despite the theoretical intractability of the SAT problem. This gap…

cs.LG2020★ 3 cited

REPAINT: Knowledge Transfer in Deep Reinforcement Learning

Yunzhe Tao, Sahika Genc, Jonathan Chung +2

Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when th…

cs.AI2020★ 1 cited

Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop

Jonathan Chung, Anna Luo, Xavier Raffin +1

We present the Battlesnake Challenge, a framework for multi-agent reinforcement learning with Human-In-the-Loop Learning (HILL). It is developed upon Battlesnake, a multiplayer ext…