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Found 4 papers, 2 papers with code
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
Specifically, the objective of adversarial data augmentation is not merely to generate data analogous to offline data distribution; instead, it aims to create adversarial examples designed to confound learned task representations and lead to incorrect task identification.
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
DORA incorporates an information bottleneck principle that maximizes mutual information between the dynamics encoding and the environmental data, while minimizing mutual information between the dynamics encoding and the actions of the behavior policy.
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
In this work, we focus on imitator learning based on only one expert demonstration.
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
A common taxonomy of existing offline RL works is policy regularization, which typically constrains the learned policy by distribution or support of the behavior policy.
