Tree based hierarchical reinforcement learning

By William T. B. Uther, Manuela M. Veloso. 2002

28 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. Manuela M. Veloso
2. Aaron M. Roth
3. William T. B. Uther
4. Nicholay Topin
5. Fernando Fernández
6. Pooyan Jamshidi
7. Peter Stone
8. Matthew E. Taylor

Citations in the corpus

• Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy (2019, background): “TTree [Uther and Veloso, 2003; Uther, 2002] solves MDPs or SMDPs and involves both state abstraction (as in our and other methods) and temporal abstraction.”
• TTree: Tree-Based State Generalization with Temporally Abstract Actions (2002, methodology): “The proofs of the lemmas 1 and 3 are available at [8].”
• Probabilistic policy reuse in a reinforcement learning agent (2006, background): “Macro-actions can be used to learn new action policies in Semi-Markov Decision Processes (SMDPs) [18].”