Gegelati: Lightweight Artificial Intelligence through Generic and Evolvable Tangled Program Graphs
Karol Desnos, Nicolas Sourbier, Pierre-Yves Raumer, Olivier Gesny, Maxime Pelcat
Abstract
Tangled Program Graph (TPG) is a reinforcement learning technique based on genetic programming concepts. On state-of-the-art learning environments, TPGs have been shown to offer comparable competence with Deep Neural Networks (DNNs), for a fraction of their computational and storage cost. This lightness of TPGs, both for training and inference, makes them an interesting model to implement Artificial Intelligences (AIs) on embedded systems with limited computational and storage resources.
Topics & Concepts
Computer scienceArtificial intelligenceArtificial neural networkGenetic programmingArtificial lifeMachine learningApplications of artificial intelligenceReinforcement learningGraphGenetic algorithmInterpretabilityComputer programmingArtificial Intelligence SystemTheoretical computer scienceDeep learningCompetence (human resources)BackpropagationEvolutionary computationComponent (thermodynamics)Computational intelligenceDeep neural networksEvolutionary Algorithms and ApplicationsReinforcement Learning in RoboticsVLSI and FPGA Design Techniques