Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
| Autor: | Julian Togelius, Jonathan Harper, Danny Lange, Hunter Henry, Arthur Juliani, Vincent Pierre Berges, Adam Crespi, Ervin Teng, Ahmed Khalifa |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
FOS: Computer and information sciences Computer Science - Machine Learning Computer science Generalization Computer Science - Artificial Intelligence media_common.quotation_subject Learning environment SIGNAL (programming language) Fidelity Machine Learning (cs.LG) 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Artificial Intelligence (cs.AI) Human–computer interaction Obstacle Benchmark (computing) Set (psychology) Tower 030217 neurology & neurosurgery media_common |
| Zdroj: | IJCAI |
| Popis: | The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to competitive video games. We propose a new benchmark - Obstacle Tower: a high fidelity, 3D, 3rd person, procedurally generated environment. An agent playing Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment. In this paper we outline the environment and provide a set of baseline results produced by current state-of-the-art Deep RL methods as well as human players. These algorithms fail to produce agents capable of performing near human level. IJCAI 2019 |
| Databáze: | OpenAIRE |
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