BOLeRo: Behavior Optimization and Learning for Robots
Alexander Fabisch, Malte Langosz, Frank Kirchner
In International Journal of Advanced Robotic Systems, SAGE Publications, volume 17, number 3, pages n.n.-n.n., 2020.
Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because
algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art
algorithms are often not publicly available though and experiments are hardly reproducible because open source
implementations are often not available or are still in a stage of research code. Consequently, often it is infeasible
to deploy these algorithms on robotic systems. BOLERO closes this gap for policy search and evolutionary algorithms
by delivering open-source implementations of behavior learning algorithms for robots. It is easy to integrate in robotic
middlewares and it can be used to compare methods and develop prototypes in simulation.
reinforcement learning, evolutionary algorithms, robotics, black-box optimization