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Hybrid of Reinforcement and Imitation Learning for Human-Like Agents
2020
IEICE transactions on information and systems
Reinforcement learning methods achieve performance superior to humans in a wide range of complex tasks and uncertain environments. However, high performance is not the sole metric for practical use such as in a game AI or autonomous driving. A highly efficient agent performs greedily and selfishly, and is thus inconvenient for surrounding users, hence a demand for human-like agents. Imitation learning reproduces the behavior of a human expert and builds a human-like agent. However, its
doi:10.1587/transinf.2019edp7298
fatcat:js3s735xcbfx7eae4w7wf734ly