Name
Title | ||
|---|---|---|
Detect, Understand, Act: A Neuro-Symbolic Hierarchical Reinforcement Learning Framework (Extended Abstract). |
Abstract | ||
|---|---|---|
We introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for effectively learning symbolic meta-policies over options using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.24963/ijcai.2022/742 | European Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Artificial Intelligence: General | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ludovico Mitchener | 1 | 0 | 0.68 |
| David Tuckey | 2 | 0 | 0.68 |
| Matthew Crosby | 3 | 0 | 0.68 |
| Alessandra Russo | 4 | 1022 | 80.10 |