This is the main repository for STEP-RL public resources, software and datasets
Planning - devising a strategy to achieve a desired objective - is one of the basic forms of intelligence. Temporal planning studies the automated synthesis of strategies when time and temporal constraints matter: it is one of the most strategic fields of Artificial Intelligence, with applications in autonomous robotics, logistics, flexible production, and many other fields.
Historically, research on temporal planning has followed a general-purpose framework: a generic engine searches for the strategy by reasoning on the problem statement (i.e. the starting condition and the desired objective), as well as on a formal model of the domain (i.e. the possible actions). Despite substantial progress in the recent years, domain-independent temporal planning still suffers from scalability issues, and fails to deal with real-word problems. The alternative is to devise ad-hoc, domain-specific solutions that, although efficient, are costly to develop, rigid to maintain, and often inapplicable in non-nominal situations.
STEP-RL will study the foundations of a new approach to Temporal Planning that will be domain-independent and efficient at the same time. The idea is to adopt a framework based on Reinforcement Learning, where a domain-independent temporal planner is specialized with respect to the domain at hand. STEP-RL will continuously improve its ability to solve temporal planning problems by learning from experience, thus becoming increasingly efficient by means of self-adaptation.
STEP-RL will advance the state of the art in temporal planning beyond the "efficiency vs flexibility" dilemma, that we faced in many industrial projects of the PSO unit.
More information about the STEP-RL project can be found in the STEP-RL project website.
The STEP-RL project is a Starting Grant funded by the European Research Council under grant agreement number 101115870. The PI of the project is Andrea Micheli.