상세정보
학술대회
A Sim2real Framework Enabling Decentralized Agents to Execute MADDPG Tasks
- 발행일
- 201912
- 출처
- Workshop on Distributed Infrastructures for Deep Learning (DIDL) 2019, pp.1-6
- 협약과제
- 19ZH1100, 사물-사람-공간의 유기적 연결을 위한 초연결 공간의 분산 지능 핵심원천 기술, 박준희
- 초록
- Multi-agent RL is a process of training the agents to collaborate with others. We argue that an additional?셱eality gap?? in the system aspects occurs when applying sim2real to the multi-agent RL, especially when performing the?셳ransferred?? collaborative task in the real-world environment. In this paper, we propose an ADO framework enabling decentralized agents to participate in performing collaborative tasks without suffering from the reality gap. Our contribution is threefold. First, we clearly identify and summarize the reality gaps in the context of the sim2real of multi-agent RL. Second, we propose a new system model to deal with system issues derived from when executing collaborative tasks. Third, we design and implement a software framework to support system issues required in developing and executing collaborative tasks in the real world.
- KSP 제안 키워드
- Collaborative task, Decentralized agents, Real-world, Reality gap, Software Framework, multi-agent, support system, system model