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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.
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.
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