Chess Game Engine Based on Reinforcement Learning

U 2017. godini je DeepMind izdao rad u kojem je opisao glavnu ideju kako ostvariti šahovski stroj temeljen na dubokom podržanom učenju. Ovaj rad se bavi implementiranjem opisanog algoritma na jedinstven način. Algoritam se temelji na dubokom podržanom učenju, odnosno na neuronskim mrežama i algoritmima podržanog učenja. U izradi se koriste rezidualne neuronske mreže koje omogućavaju izradu vrlo dubokih mreža, tj. mreža s puno slojeva. Korišteni algoritam podržanog učenja je obilazak stabla tehnikom Monte Carlo. Implementacijom neuronskih mreža i Monte Carlo algoritma se gradi model šahovskog stroja te se on uči koji su potezi dobri na temelju nagrada i kazni koje dobije u partijama koje igra sam sa sobom. Izgrađeni stroj ima još dosta mjesta za nadogradnje. In 2017, DeepMind published a paper describing the main idea of how to realize a chess machine based on deep reinforcement learning. This paper deals with the implementation of the described algorithm in a unique way. The algorithm is based on deep reinforcement learning, i.e. neural networks and reinforcement learning algorithms. Residual neural networks are used in designing the model, which enable very deep networks, i.e. networks with many layers. The supported learning algorithm used is a tree tour using the Monte Carlo technique. By implementing neural networks and the Monte Carlo algorithm, a model of a chess machine is built and one learns which moves are good based on the reward and punishment received in the games they play with. The built machine still has plenty of room to upgrade.