Removing the Target Network from Deep Q-Networks with the Mellowmax Operator
adaptive agents and multi-agents systems(2019)
摘要
Deep Q-Network (DQN) is a learning algorithm that achieves human-level performance in high-dimensional domains like Atari games. We propose that using an softmax operator, Mellowmax, in DQN reduces its need for a separate target network, which is otherwise necessary to stabilize learning. We empirically show that, in the absence of a target network, the combination of Mellowmax and DQN outperforms DQN alone.
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