A Comparison of h 2 and MMM for Mutex Pair Detection Applied to Pattern Databases

Sadeqi, Mehdi; Holte, Robert C.; Zilles, Sandra

doi:10.1007/978-3-319-06483-3_20

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…This motivates the development of an efficient mutex pair detection method for multi-valued domains. Two such methods are proposed in (Sadeqi et al, 2013a(Sadeqi et al, , 2014 and (Sadeqi et al, 2013b).…”

Section: 880 Spurious States Improves the Average Heuristic Value (Se...mentioning

confidence: 99%

An Empirical Study of the Effects of Spurious Transitions on Abstraction-based Heuristics

Sadeqi,

Holte,

Zilles

2017

Preprint

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The efficient solution of state space search problems is often attempted by guiding search algorithms with heuristics (estimates of the distance from any state to the goal). A popular way for creating heuristic functions is by using an abstract version of the state space. However, the quality of abstraction-based heuristic functions, and thus the speed of search, can suffer from spurious transitions, i.e., state transitions in the abstract state space for which no corresponding transitions in the reachable component of the original state space exist. Our first contribution is a quantitative study demonstrating that the harmful effects of spurious transitions on heuristic functions can be substantial, in terms of both the increase in the number of abstract states and the decrease in the heuristic values, which may slow down search. Our second contribution is an empirical study on the benefits of removing a certain kind of spurious transition, namely those that involve states with a pair of mutually exclusive (mutex) variablevalue assignments. In the context of state space planning, a mutex pair is a pair of variable-value assignments that does not occur in any reachable state. Detecting mutex pairs is a problem that has been addressed frequently in the planning literature. Our study shows that there are cases in which mutex detection helps to eliminate harmful spurious transitions to a large extent and thus to speed up search substantially.

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Section: 880 Spurious States Improves the Average Heuristic Value (Se...mentioning

confidence: 99%

An Empirical Study of the Effects of Spurious Transitions on Abstraction-based Heuristics

Sadeqi,

Holte,

Zilles

2017

Preprint

Self Cite

View full text Add to dashboard Cite

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GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling

Chitnis

Silver

Tenenbaum

et al. 2021

AAAI

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We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-literal babbling (GLIB), a simple and general method for exploration in such problems. GLIB samples relational conjunctive goals that can be understood as specific, targeted effects that the agent would like to achieve in the world, and plans to achieve these goals using the transition model being learned. We provide theoretical guarantees showing that exploration with GLIB will converge almost surely to the ground truth model. Experimentally, we find GLIB to strongly outperform existing methods in both prediction and planning on a range of tasks, encompassing standard PDDL and PPDDL planning benchmarks and a robotic manipulation task implemented in the PyBullet physics simulator. Video: https://youtu.be/F6lmrPT6TOY Code: https://git.io/JIsTB

show abstract

A Comparison of h 2 and MMM for Mutex Pair Detection Applied to Pattern Databases

Cited by 2 publications

References 12 publications

An Empirical Study of the Effects of Spurious Transitions on Abstraction-based Heuristics

An Empirical Study of the Effects of Spurious Transitions on Abstraction-based Heuristics

GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling

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