W niniejszej pracy przedstawiam wynik serii eksperymentów mających na celu zbadanie skuteczności i niezawodności prostego wariantu algorytmu uczenia ze wzmocnieniem Deep Q-Networks. Zadaniem algorytmu jest uczenie sztucznej sieci neuronowej sterowania pojazdami w symulowanej przestrzeni kosmicznej.In this paper, I present the result of a series of experiments aimed at examining the effectiveness and reliability of a simple variant of the Deep Q-Networks reinforcement learning algorithm. The task of the algorithm is to teach the artificial neural network to control vehicles in simulated outer space
Cilj je ovoga diplomskog rada objasniti učenje podrškom - paradigmu učenja neuronskih mreža koja se ...
The increased availability of computing power have made reinforcement learning a popular field of sc...
Abstract Deep reinforcement learning is poised to be a revolutionised step towards newer possibiliti...
Razvoj sustava koji sami uče jedan je od važnijih problema računarske znanosti. Poseban izazov preds...
The aim of this bachelor thesis is to teach a neural network solving classic control theory problems...
U ovom radu, održavanje formacije multirobotskog sustava realizirano je korištenjem pristupa vođa-pr...
Algorytmy uczenia się przez wzmacnianie są wykorzystywane do rozwiązywania problemów o stale rosnący...
Deep Reinforcement Learning (DRL) is attracting increasing interest due to its ability to learn how ...
Premi HEMAV 2018 al millor TFGRecent improvements in computation and algorithmic research, together ...
Research on reinforcement learning algorithms to play complex video games have brought forth control...
In the ¯eld of machine learning, reinforcement learning constitutes the idea of enabling machines to...
Using reinforcement learning as a part of a Guidance, Navigation and Control (GNC) system is a relat...
Uczenie przez wzmacnianie jest jedną z gałęzi uczenia maszynowego w której celem jest znalezienie op...
International audienceWe present the results of a research aimed at improving the Q-learning method ...
U ovom je radu obrađeno podržano učenje kao vrsta strojnog učenja čiji je cilj maksimizirati ukupnu ...
Cilj je ovoga diplomskog rada objasniti učenje podrškom - paradigmu učenja neuronskih mreža koja se ...
The increased availability of computing power have made reinforcement learning a popular field of sc...
Abstract Deep reinforcement learning is poised to be a revolutionised step towards newer possibiliti...
Razvoj sustava koji sami uče jedan je od važnijih problema računarske znanosti. Poseban izazov preds...
The aim of this bachelor thesis is to teach a neural network solving classic control theory problems...
U ovom radu, održavanje formacije multirobotskog sustava realizirano je korištenjem pristupa vođa-pr...
Algorytmy uczenia się przez wzmacnianie są wykorzystywane do rozwiązywania problemów o stale rosnący...
Deep Reinforcement Learning (DRL) is attracting increasing interest due to its ability to learn how ...
Premi HEMAV 2018 al millor TFGRecent improvements in computation and algorithmic research, together ...
Research on reinforcement learning algorithms to play complex video games have brought forth control...
In the ¯eld of machine learning, reinforcement learning constitutes the idea of enabling machines to...
Using reinforcement learning as a part of a Guidance, Navigation and Control (GNC) system is a relat...
Uczenie przez wzmacnianie jest jedną z gałęzi uczenia maszynowego w której celem jest znalezienie op...
International audienceWe present the results of a research aimed at improving the Q-learning method ...
U ovom je radu obrađeno podržano učenje kao vrsta strojnog učenja čiji je cilj maksimizirati ukupnu ...
Cilj je ovoga diplomskog rada objasniti učenje podrškom - paradigmu učenja neuronskih mreža koja se ...
The increased availability of computing power have made reinforcement learning a popular field of sc...
Abstract Deep reinforcement learning is poised to be a revolutionised step towards newer possibiliti...