A sim2real framework enabling decentralized agents to execute MADDPG tasks
Young‐Ho Suh, Sungpil Woo, Hyunhak Kim, Dong-Hwan Park · 2019
Multi-agent RL is a process of training the agents to collaborate with others. We argue that an additional 'reality gap' in the system aspects occurs when applying sim2real to the multi-agent RL, especially when performing the 'transferred' 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.