Reinforcement learning holds the promise of enabling autonomous robots to
learn large repertoires of behavioral skills with minimal human intervention.
However, robotic applications of reinforcement learning often compromise the
autonomy of the learning process in favor of achieving training times that are
practical for real physical systems. This typically involves introducing
hand-engineered policy representations and human-supplied demonstrations. Deep
reinforcement learning alleviates this limitation by training general-purpose
neural network policies, but applications of direct deep reinforcement learning
algorithms have so far been restricted to simulated settings and relatively
simple tasks, due to their apparent high sample complexity. In this paper, we
demonstrate that a recent deep reinforcement learning algorithm based on
off-policy training of deep Q-functions can scale to complex 3D manipulation
tasks and can learn deep neural network policies efficiently enough to train on
real physical robots. We demonstrate that the training times can be further
reduced by parallelizing the algorithm across multiple robots which pool their
policy updates asynchronously. Our experimental evaluation shows that our
method can learn a variety of 3D manipulation skills in simulation and a
complex door opening skill on real robots without any prior demonstrations or
manually designed representations.

本文介绍一种基于深度 Q 函数算法的深度强化学习方法，能够实现在真实的物理机器人上进行复杂的 3D 操作任务学习，并通过多个机器人异步汇聚优化策略更新等技术进一步提高训练效率。