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Ensemble Network Architecture for Deep Reinforcement Learning
2018
Mathematical Problems in Engineering
The popular deep Q learning algorithm is known to be instability because of the Q-value's shake and overestimation action values under certain conditions. These issues tend to adversely affect their performance. In this paper, we develop the ensemble network architecture for deep reinforcement learning which is based on value function approximation. The temporal ensemble stabilizes the training process by reducing the variance of target approximation error and the ensemble of target values
doi:10.1155/2018/2129393
fatcat:gepes2guczegneqhfzholcg2qm