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Found 6 papers, 1 papers with code
Towards a Unified Framework for Sequential Decision Making
In recent years, the integration of Automated Planning (AP) and Reinforcement Learning (RL) has seen a surge of interest.
On Using Admissible Bounds for Learning Forward Search Heuristics
This results in a different loss function from the MSE commonly employed in the literature, which implicitly models the learned heuristic as a gaussian distribution.
A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making
Conversely, Reinforcement Learning (RL) proposes to learn the solution of the SDP from data, without a world model, and represent the learned knowledge subsymbolically.
NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems
In this paper we propose NeSIG, to the best of our knowledge the first domain-independent method for automatically generating planning problems that are valid, diverse and difficult to solve.
Learning Numerical Action Models from Noisy Input Data
The algorithm presented here improves the learning capabilities of PlanMiner when using noisy data as input.
Goal Reasoning by Selecting Subgoals with Deep Q-Learning
In this work we propose a goal reasoning method which learns to select subgoals with Deep Q-Learning in order to decrease the load of a planner when faced with scenarios with tight time restrictions, such as online execution systems.
