상세정보
학술대회
A Survey on Simulation Environments for Reinforcement Learning
- 발행일
- 202107
- 출처
- International Conference on Ubiquitous Robots (UR) 2021, pp.1-5
- 협약과제
- 21HS1700, 실환경 서비스 상황에서 사용자 반응에 지속적으로 지역(Local) 적응하는 로봇 지능 기술 개발, 장민수
- 초록
- Most of the recent studies of reinforcement learning and robotics basically employ computer simulation due to the advantages of time and cost. For this reason, users have to spare time for investigation in order to choose optimal environment for their purposes. This paper presents a survey result that can be a guidance in user's choice for simulation environments. The investigation result includes features, brief historical backgrounds, license policies and formats for robot and object description of the eight most popular environments in robot RL studies. We also propose a quantitative evaluation method for those simulation environments considering the features and a pragmatic point of view.
- KSP 제안 키워드
- Computer simulation(MC and MD), Quantitative evaluation method, Reinforcement Learning(RL), object description