Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation
- Journal
- DSpace@MIT (Massachusetts Institute of Technology)
In The Last Decade
doi.org/w8110407 →Countries where authors are citing Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation
This map shows the geographic impact of Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation more than expected).
Fields of papers citing Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation
This network shows the impact of Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation.
About Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation
This paper, published in 2016, received 318 indexed citations . Written by Tejas D. Kulkarni, Karthik Narasimhan, Ardavan Saeedi and Joshua B. Tenenbaum covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (231 citations), Control and Systems Engineering (73 citations) and Computer Vision and Pattern Recognition (64 citations). Published in DSpace@MIT (Massachusetts Institute of Technology).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w8110407.