边移动边思考：带并发控制的深度强化学习
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
摘要：
我们研究强化学习的设置，从策略中采样的动作必须与受控系统的时间演化同时进行，例如当机器人必须决定下一个动作，同时仍然执行前一个动作。就像人或动物一样，机器人必须同时思考和移动，在前一个动作完成之前决定下一个动作。为了开发一个算法框架，这样的并发控制问题，我们开始与连续时间的贝尔曼方程的制定，然后离散化的方式，是知道系统延迟。我们通过对现有基于值的深度强化学习算法的简单架构扩展来实例化这类新的近似动态规划方法。我们评估我们的方法模拟基准任务和大型机器人抓取任务，机器人必须“思考，而移动”。
英文摘要：
We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action. Much like a person or an animal, the robot must think and move at the same time, deciding on its next action before the previous one has completed. In order to develop an algorithmic framework for such concurrent control problems, we start with a continuous-time formulation of the Bellman equations, and then discretize them in a way that is aware of system delays. We instantiate this new class of approximate dynamic programming methods via a simple architectural extension to existing value-based deep reinforcement learning algorithms. We evaluate our methods on simulated benchmark tasks and a large-scale robotic grasping task where the robot must "think while moving".
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