DIMSAN: Fast Exploration with the Synergy between Density-based Intrinsic Motivation and Self-adaptive Action Noise
Jiayi Li Boyao Li Tao Lu Ning Lu Yinghao Cai Shuo Wang Jiayi Li Boyao Li Tao Lu Ning Lu Yinghao Cai Shuo Wang
Exploration in environments with sparse rewards remains a challenging problem in Deep Reinforcement Learning (DRL). For the off-policy method, it usually needs a large number of training samples. With the growing dimensions of state and action space, this method becomes more and more sample-inefficient. In this paper, we propose a novel fast exploration method for off-policy reinforcement learning...