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Found 4 papers, 4 papers with code
Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
In this work, we introduce DCRL-MAP-Elites, an extension of DCG-MAP-Elites that utilizes the descriptor-conditioned actor as a generative model to produce diverse solutions, which are then injected into the offspring batch at each generation.
MAP-Elites with Descriptor-Conditioned Gradients and Archive Distillation into a Single Policy
Quality-Diversity algorithms, such as MAP-Elites, are a branch of Evolutionary Computation generating collections of diverse and high-performing solutions, that have been successfully applied to a variety of domains and particularly in evolutionary robotics.
SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning
This paper presents the proof of concept for SeaPearl, a new CP solver implemented in Julia, that supports machine learning routines in order to learn branching decisions using reinforcement learning.
Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization
This paper proposes a novel algorithm, QDPG, which combines the strength of Policy Gradient algorithms and Quality Diversity approaches to produce a collection of diverse and high-performing neural policies in continuous control environments.
