The coupling of deep reinforcement learning to numerical flow control problems has recently received a considerable attention, leading to groundbreaking results and opening new perspectives for the domain. Due to the usually high computational cost of fluid dynamics solvers, the use of parallel environments during the learning process represents an essential ingredient to attain efficient control in a reasonable time. Yet, most of the deep reinforcement learning literature for flow control relies on on-policy algorithms, for which the massively parallel transition collection may break theoretical assumptions and lead to suboptimal control models. To overcome this issue, we propose a parallelism pattern relying on partial-trajectory buffers terminated by a return bootstrapping step, allowing a flexible use of parallel environments while preserving the on-policiness of the updates. This approach is illustrated on a CPU-intensive continuous flow control problem from the literature.

翻译：将深度强化学习与数值流动控制问题相结合近期受到广泛关注，取得了突破性成果并为该领域开辟了新的前景。由于流体动力学求解器通常计算成本高昂，学习过程中使用并行环境是在合理时间内实现高效控制的关键要素。然而，目前流动控制领域的大多数深度强化学习研究依赖于在策略算法，这类算法中大规模的并行状态转移收集可能破坏理论假设并导致次优控制模型。为解决该问题，我们提出了一种基于部分轨迹缓冲区的并行模式，该缓冲区通过回报自助抽样步骤终止，可在保持更新在策略性的同时灵活使用并行环境。该方法通过文献中一个计算密集型的连续流动控制问题进行实例验证。