Sim-and-real training is a promising alternative to sim-to-real training for
robot manipulations. However, the current sim-and-real training is neither
efficient, i.e., slow convergence to the optimal policy, nor effective, i.e.,
sizeable real-world robot data. Given limited time and hardware budgets, the
performance of sim-and-real training is not satisfactory. In this paper, we
propose a Consensus-based Sim-And-Real deep reinforcement learning algorithm
(CSAR) for manipulator pick-and-place tasks, which shows comparable performance
in both sim-and-real worlds. In this algorithm, we train the agents in
simulators and the real world to get the optimal policies for both sim-and-real
worlds. We found two interesting phenomenons: (1) Best policy in simulation is
not the best for sim-and-real training. (2) The more simulation agents, the
better sim-and-real training. The experimental video is available at:
this https URL.

本文提出了一个基于深度强化学习的机器人 pick-and-place 任务的共识型模拟现实联合训练算法（CSAR），目的是在模拟和实际环境中都实现高效和有效的策略优化。实验表明，模拟中的最佳策略并不一定适用于模拟和实际环境的学习。同时，越多的模拟代理越有利于模拟现实的训练。