Data-Efficient Scalable Reinforcement Learning for Practical Robotic Environments
Source: cordis.europa.eu Designing algorithms for more challenging data Machine Learning researchers often have to overcome the ‘sim-to-real’ transfer, where algorithmic feats accomplished in computer simulations can be repeated in test performances. DESIRE has produced a data-driven, robust decision-making algorithm to achieve just that. Advancements in computing, such as the game AlphaGo, both rely on and Read More