Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations

1 Apr 2020Zhuangdi ZhuKaixiang LinBo DaiJiayu Zhou

Model-free deep reinforcement learning (RL) has demonstrated its superiority on many complex sequential decision-making problems. However, heavy dependence on dense rewards and high sample-complexity impedes the wide adoption of these methods in real-world scenarios... (read more)

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