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Found 7 papers, 3 papers with code
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity
Quality-Diversity is a branch of stochastic optimization that is often applied to problems from the Reinforcement Learning and control domains in order to construct repertoires of well-performing policies/skills that exhibit diversity with respect to a behavior space.
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control
Model-based RL/control have gained significant traction in robotics.
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations
Model-based Reinforcement Learning and Control have demonstrated great potential in various sequential decision making problem domains, including in robotics settings.
Geodesics, Non-linearities and the Archive of Novelty Search
An argument that is often encountered in the literature is that the archive prevents exploration from backtracking or cycling, i. e. from revisiting previously encountered areas in the behavior space.
Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms
Inspired by recent works on Reinforcement Learning benchmarks, we argue that the identification of challenges faced by QD methods and the development of targeted, challenging, scalable but affordable benchmarks is an important step.
Few-shot Quality-Diversity Optimization
Experiments carried in both sparse and dense reward settings using robotic manipulation and navigation benchmarks show that it considerably reduces the number of generations that are required for QD optimization in these environments.
BR-NS: an Archive-less Approach to Novelty Search
In this paper, we discuss an alternative approach to novelty estimation, dubbed Behavior Recognition based Novelty Search (BR-NS), which does not require an archive, makes no assumption on the metrics that can be defined in the behavior space and does not rely on nearest neighbours search.
