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Found 4 papers, 1 papers with code
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels
In this paper, we combine recent developments in Deep Kernel Learning (DKL) and attention-based Transformer models to improve the modeling powers of GP surrogates with meta-learning.
Meta-Reinforcement Learning for Heuristic Planning
In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks.
Information-theoretic Task Selection for Meta-Reinforcement Learning
In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to prepare for and learn faster in new, unseen, but related tasks.
Curriculum Learning for Cumulative Return Maximization
We propose a task sequencing algorithm maximizing the cumulative return, that is, the return obtained by the agent across all the learning episodes.
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