In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is dexterous manipulation tasks in robotics which are difficult to solve using traditional control theory or hand-engineered approaches. One example of such a task is to grasp an object and precisely stack it on another. Solving this difficult and practically relevant problem in the real world is an important long-term goal for the field of robotics. Here we take a step towards this goal by examining the problem in simulation and providing models and techniques aimed at solving it. We introduce two extensions to the Deep Deterministic Policy Gradient algorithm (DDPG), a model-free Q-learning based method, which make it significantly more data-efficient and scalable. Our results show that by making extensive use of off-policy data and replay, it is possible to find control policies that robustly grasp objects and stack them. Further, our results hint that it may soon be feasible to train successful stacking policies by collecting interactions on real robots.
[A]: Data-efficient Deep Reinforcement Learning for Dexterous Manipulation

[Q]: The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While techniques to solve Distributed Constraint Optimization Problems (DCOPs) are abundant and have matured substantially since the field inception, the number of DCOP realistic applications and benchmark used to asses the performance of DCOP algorithms is lagging behind. To contrast this background we (i) introduce the Smart Home Device Scheduling (SHDS) problem, which describe the problem of coordinating smart devices schedules across multiple homes as a multi-agent system, (ii) detail the physical models adopted to simulate smart sensors, smart actuators, and homes environments, and (iii) introduce a DCOP realistic benchmark for SHDS problems.
[A]: A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs

[Q]: While influence diagrams have many ad vantages as a representation framework for Bayesian decision problems, they have a se rious drawback in handling asymmetric de cision problems. To be represented in an influence diagram, an asymmetric decision problem must be symmetrized. A consid erable amount of unnecessary computation may be involved when a symmetrized influ ence diagram is evaluated by conventional al gorithms. In this paper we present an ap proach for avoiding such unnecessary compu tation in influence diagram evaluation.
[A]:
Solving Asymmetric Decision Problems with Influence Diagrams