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Found 6 papers, 2 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.
Planning from video game descriptions
This project proposes a methodology for the automatic generation of action models from video game dynamics descriptions, as well as its integration with a planning agent for the execution and monitoring of the plans.
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.
