Search Results for author:
Found 4 papers, 0 papers with code
Offline Reinforcement Learning with Adaptive Behavior Regularization
Offline reinforcement learning (RL) defines a sample-efficient learning paradigm, where a policy is learned from static and previously collected datasets without additional interaction with the environment.
Yordle: An Efficient Imitation Learning for Branch and Bound
Combinatorial optimization problems have aroused extensive research interests due to its huge application potential.
Learning to Reformulate for Linear Programming
In the past decades, a serial of traditional operation research algorithms have been proposed to obtain the optimum of a given LP in a fewer solving time.
An Improved Reinforcement Learning Algorithm for Learning to Branch
Similar to offline reinforcement learning, we initially train on the demonstration data to accelerate learning massively.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
