Learning Embedded Maps of Markov Processes

By Yaakov Engel, Shie Mannor. 2001

19 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. Nir Ben-Zrihem
2. Tom Zahavy
3. Chen Tessler
4. Erez Schwartz
5. Daniel Jaymin Mankowitz
6. Prithviraj Ammanabrolu
7. Gabriel Dulac-Arnold
8. Shie Mannor

Citations in the corpus

• Graying the black box: Understanding DQNs (2016, background): “Similar to (Engel & Mannor, 2001) we visualize the dynamics of the learned policy.”
• Language is Power: Representing States Using Natural Language in Reinforcement Learning. (2019, background): “…State Representations (PSR) [Littman and Sutton, 2002], which capture the state as a vector of predictions of future outcomes, and a Heuristic Embedding of Markov Processes (HEMP) [Engel and Mannor, 2001], which learns to embed transition probabilities using an energy-based optimization problem.”
• The Natural Language of Actions (2019, unknown): “…State Representations (PSR) (Littman & Sutton, 2002), which capture the state as a vector of predictions of future outcomes, and a Heuristic Embedding of Markov Processes (HEMP) (Engel & Mannor, 2001), which learns to embed transition probabilities using an energy-based optimization problem.”