BROWSE
Detail
Journal Article
역강화학습 기술 동향
- Issue Date
- 2019-12
- Citation
- 전자통신동향분석, v.34, no.6, pp.100-107
- ISSN
- 1225-6455
- Publisher
- 한국전자통신연구원
- Language
- Korean
- Type
- Journal Article
- Abstract
- Recently, reinforcement learning (RL) has expanded from the research phase of the virtual simulation environment to a wide range of applications, such as autonomous driving, natural language processing, recommendation systems, and disease diagnosis. However, RL is less likely to be used in these complex real-world environments. In contrast, inverse reinforcement learning (IRL) can obtain optimal policies in various situations; furthermore, it can use expert demonstration data to achieve its target task. In particular, IRL is expected to be a key technology for artificial general intelligence research that can successfully perform human intellectual tasks. In this report, we briefly summarize various IRL techniques and research directions.
- KSP Keywords
- Artificial general intelligence, Disease Diagnosis, Inverse Reinforcement learning, Key technology, Natural Language Processing(NLP), Optimal policy, Real-world, Recommendation system, Reinforcement learning(RL), Simulation Environment, Virtual simulation
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
