Hierarchical Memory-Based Reinforcement Learning

By Natalia Hernandez-Gardiol, Sridhar Mahadevan. 2000

65 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sergey Levine
2. Yoshua Bengio
3. Josh Tenenbaum
4. J. Urgen Schmidhuber
5. Ruslan Salakhutdinov
6. Abhinav Kumar Gupta
7. Jianfeng Gao
8. Karthik Narasimhan

Citations in the corpus

• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation (2016, background): “Hernandez and Mahadevan [13] combine hierarchies with short-term memory to handle partial observations.”
• Recent Advances in Hierarchical Reinforcement Learning (2003, methodology): “Among the termination schemes studied by Rohanimanesh and Mahadevan [57] are the following two.”
• Hierarchical Approaches (2012, methodology): “Wiering and Schmidhuber (1997) decompose a POMDP into sequences of simpler subtasks, Hernandez and Mahadevan (2000) solve partially observable sequential decision tasks by propagating reward across long decision sequences using a memory-based SMDP. Pineau and Thrun (2002) present an algorithm for…”