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Constrained Deep Q-Learning Gradually Approaching Ordinary Q-Learning
2019
Frontiers in Neurorobotics
A deep Q network (DQN) (Mnih et al., 2013) is an extension of Q learning, which is a typical deep reinforcement learning method. In DQN, a Q function expresses all action values under all states, and it is approximated using a convolutional neural network. Using the approximated Q function, an optimal policy can be derived. In DQN, a target network, which calculates a target value and is updated by the Q function at regular intervals, is introduced to stabilize the learning process. A less
doi:10.3389/fnbot.2019.00103
pmid:31920613
pmcid:PMC6914867
fatcat:b7ujdkvl3zc7jbgdrqqglnyplu