Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner

By Kurt Driessens, J. Ramon, Hendrik Blockeel. 2001

96 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Fitted Q-Learning for Relational Domains (2020, methodology): “This work was later extended by others (Driessens and Ramon 2003; Driessens and Dˇzeroski 2004; Driessens, Ramon, and Blockeel 2001; G¨artner, Driessens, and Ramon 2003) w.r.t building incremental RRTs, providing guidance to the learner and regression algorithms using kernels and gaus-sian…”
A review of learning planning action models (2018, methodology): “It uses the TG algorithm (Driessens et al. (2001)) to learn relational decision trees.”
Integrating Guidance into Relational Reinforcement Learning (2004, methodology): “The TG-algorithm create a leaf with empty statistics for each data point that becomes available do classify data point down to a leaf update statistics in this leaf and discard data point if split needed in updated leaf then use best test to construct node grow two new leafs with empty statistics”
Graph kernels and Gaussian processes for relational reinforcement learning (2006, methodology): “RRL-Tg [8] uses an incremental first order regression tree algorithm Tg to construct the Q-function.”
Solving Relational and First-Order Logical Markov Decision Processes: A Survey (2012, methodology): “An incremental extension to Q-RRL is the TG-algorithm (Driessens et al, 2001).”
Non-parametric policy gradients: a unified treatment of propositional and relational domains (2008, methodology): “Basically, a number of relational regression algorithms have been developed for use in this RL system that employ relational regression trees (Driessens et al., 2001), relational instance based regression (Driessens & Ramon, 2003), graph kernels and Gaussian processes (Gärtner et al.”
Relational Reinforcement Learning An Overview (2004, methodology): “The TG algorithm (Driessens et al., 2001) is a combination of the Tilde algorithm (Blockeel & De Raedt, 1998) that builds rst order classiication and regression trees and the G-algorithm (Chapman & Kaelbling , 1991) that uses a number of statistical values concerning the performance of each…”
Relational Instance Based Regression for Relational Reinforcement Learning (2003, methodology): “RRL-Tg never succeeded in this without the use of explicit policy learning (P-learning) (Džeroski et al., 1998; Driessens et al., 2001).”
Relational reinforcement learning (2001, unknown): “gression trees [5] using a number of incrementally updated statistics to build a relational regression tree.”