The optimization of functions to find the best solution according to one or several objectives has a central role in many engineering and research fields. Recently, a new family of optimization algorithms, named Quality-Diversity optimization, has been introduced, and contrasts with classic algorithms. Instead of searching for a single solution, Quality-Diversity algorithms are searching for a large collection of both diverse and high-performing solutions. The role of this collection is to cover the range of possible solution types as much as possible, and to contain the best solution for each type. The contribution of this paper is threefold. Firstly, we present a unifying framework of Quality-Diversity optimization algorithms that covers the two main algorithms of this family (Multi-dimensional Archive of Phenotypic Elites and the Novelty Search with Local Competition), and that highlights the large variety of variants that can be investigated within this family. Secondly, we propose algorithms with a new selection mechanism for Quality-Diversity algorithms that outperforms all the algorithms tested in this paper. Lastly, we present a new collection management that overcomes the erosion issues observed when using unstructured collections. These three contributions are supported by extensive experimental comparisons of Quality-Diversity algorithms on three different experimental scenarios.
Based on this observation, a variant of NSLC, named Behavioral Repertoire Evolution''(BR-Evolution @cite_36 ), has been introduced to progressively improve the archive's quality by replacing the solutions that are kept in the archive with better ones as soon as they are found. This approach has been applied to generate Behavioral Repertoires'' in robotics, which consists of a large collection of diverse, but effective, behaviors for a robotic agent in a single run of an evolutionary algorithm. It has also been used to produce collections of walking gaits, allowing a virtual six-legged robot to walk in every direction and at different speeds. The descriptor space is defined as the final position of the robot after walking for 3 seconds, while the quality score corresponds to an orientation error. As we reproduce this experiment in this paper, we provide additional descriptions and technical details in section .
@cite_36
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Learning in robotics typically involves choosing a simple goal (e.g. walking) and assessing the performance of each controller with regard to this task (e.g. walking speed). However, learning advanced, input-driven controllers (e.g. walking in each direction) requires testing each controller on a large sample of the possible input signals. This costly process makes difficult to learn useful low-level controllers in robotics. Here we introduce BR-Evolution, a new evolutionary learning technique that generates a behavioral repertoire by taking advantage of the candidate solutions that are usually discarded. Instead of evolving a single, general controller, BR-evolution thus evolves a collection of simple controllers, one for each variant of the target behavior; to distinguish similar controllers, it uses a performance objective that allows it to produce a collection of diverse but high-performing behaviors. We evaluated this new technique by evolving gait controllers for a simulated hexapod robot. Results show that a single run of the EA quickly finds a collection of controllers that allows the robot to reach each point of the reachable space. Overall, BR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.