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Found 9 papers, 2 papers with code
Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning
Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance.
Efficient Constraint Generation for Stochastic Shortest Path Problems
Current methods for solving Stochastic Shortest Path Problems (SSPs) find states' costs-to-go by applying Bellman backups, where state-of-the-art methods employ heuristics to select states to back up and prune.
Learning Domain-Independent Heuristics for Grounded and Lifted Planning
We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search.
Heuristic Search for Multi-Objective Probabilistic Planning
Heuristic search is a powerful approach that has successfully been applied to a broad class of planning problems, including classical planning, multi-objective planning, and probabilistic planning modelled as a stochastic shortest path (SSP) problem.
Learning Domain-Independent Planning Heuristics with Hypergraph Networks
We present the first approach capable of learning domain-independent planning heuristics entirely from scratch.
ASNets: Deep Learning for Generalised Planning
In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets).
Action Schema Networks: Generalised Policies with Deep Learning
In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems.
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Policy synthesis addresses the problem of how to control or limit the decisions an agent makes so that a given specification is met.
Trajectory-Based Short-Sighted Probabilistic Planning
In order to compute a solution for a probabilistic planning problem, planners need to manage the uncertainty associated with the different paths from the initial state to a goal state.
