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Found 3 papers, 3 papers with code
The balancing principle for parameter choice in distance-regularized domain adaptation
Our approach starts with the observation that the widely-used method of minimizing the source error, penalized by a distance measure between source and target feature representations, shares characteristics with regularized ill-posed inverse problems.
Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning
Algorithms that constrain the learned policy towards the given dataset perform well for datasets with high TQ or SACo.
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Align-RUDDER outperforms competitors on complex artificial tasks with delayed reward and few demonstrations.
General Reinforcement Learning
Multiple Sequence Alignment
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