Initial Progress in Transfer for Deep Reinforcement Learning Algorithms
semanticscholar(2016)
摘要
As one of the first successful models that combines reinforcement learning technique with deep neural networks, the Deep Q-network (DQN) algorithm has gained attention as it bridges the gap between high-dimensional sensor inputs and autonomous agent learning. However, one main drawback of DQN is the long training time required to train a single task. This work aims to leverage transfer learning (TL) techniques to speed up learning in DQN. We applied this technique in two domains, Atari games and cart-pole, and show that TL can improve DQN’s performance on both tasks without altering the network structure.
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