Serious Games (SG) belong to the most important future e-learning trends; they attain enhanced public acceptance and importance. Although more frequently used in recruitment and training, their production is still effortful and expensive. The generation of human behaviour and general game playing remain prevalent trends and challenges. Serious games can profit from diverse behaviour to increase learning effectiveness and from general AI methods for easy adaption to different games. Deep reinforcement learning (DRL) offers an opportunity for application because it has shown considerable results and is widely applicable as a general method. DRL means the combination of reinforcement learning and deep learning methods. A famous example is deep Q-learning for learning to play Atari games, where a convolutional neural network was trained with a variant of Q-learning on different Atari games and partially outperformed human game players.    «

Serious Games (SG) belong to the most important future e-learning trends; they attain enhanced public acceptance and importance. Although more frequently used in recruitment and training, their production is still effortful and expensive. The generation of human behaviour and general game playing remain prevalent trends and challenges. Serious games can profit from diverse behaviour to increase learning effectiveness and from general AI methods for easy adaption to different games. Deep reinforceme...    »