4 papers
Subgoaling Relaxation-based Heuristics for Numeric Planning with Infinite Actions
Ángel Aso-Mollar, Diego Aineto, Enrico Scala +1
Numeric planning with control parameters extends the standard numeric planning model by introducing action parameters as free numeric variables that must be instantiated during pla…
Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions
Ángel Aso-Mollar, Diego Aineto, Enrico Scala +1
In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables. Existing state-of-the-art approa…
An Efficient Approach for Cooperative Multi-Agent Learning Problems
Ángel Aso-Mollar, Eva Onaindia
In this article, we propose a centralized Multi-Agent Learning framework for learning a policy that models the simultaneous behavior of multiple agents that need to coordinate to s…
Meta-operators for Enabling Parallel Planning Using Deep Reinforcement Learning
Ángel Aso-Mollar, Eva Onaindia
There is a growing interest in the application of Reinforcement Learning (RL) techniques to AI planning with the aim to come up with general policies. Typically, the mapping of the…