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...
