今天给大家分享的一篇我们最近做的有意思的文章,文章标题是: D2SR: Transferring Dense Reward Function to Sparse by Network Resetting。 欢迎大家引用!为了让大家更方便的阅读和理解,我不仅写了这篇博客,…
The pseudo target-achieving reward converts the sparse reward into dense reward, thus the long-horizon difficulty is alleviated. The whole system makes hierarchical decisions, in which a high-level conductor travels through different targets, and a low-level executor operates in the original action ...
We show that our approach outperforms state-of-the-art techniques for combining behavior cloning and reinforcement learning for both dense and sparse reward scenarios. Our results also suggest that directly including the behavior cloning loss on demonstration data helps to ensure stable learning and ...
These strategies do not reward many real-world instances in which data models are very time-dependent. Therefore, data-driven machine learning and deep learning solutions are required, which allow for more flexible modifications. The third subcategory is machine learning and deep learning models, ...
algorithms Article Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning Simone Parisi 1,* , Davide Tateo 2 , Maximilian Hensel 2, Carlo D'Eramo 2, Jan Peters 2 and Joni Pajarinen 3 1 Meta AI Research, 4720 Forbes Avenue, Pittsburgh, PA 15213, USA 2 ...
Frequently emitted rewards are called “dense”, in contrast to infrequent emissions which are called “sparse”. Since improving the policy relies on getting feedback via rewards, the policy cannot be improved until a reward is obtained. In situations where this occurs very rarely, the agent ...