Parallel data collection has redefined Reinforcement Learning (RL), unlocking unprecedented efficiency and powering breakthroughs in large-scale real-world applications. In this paradigm, $N$ identical agents operate in $N$ replicas of an environment simulator, accelerating data collection by a factor of $N$. A critical question arises: Does specializing the policies of the parallel agents hold the key to surpass the $N$ factor acceleration? In this paper, we introduce a novel learning framework that maximizes the entropy of collected data in a parallel setting. Our approach carefully balances the entropy of individual agents with inter-agent diversity, effectively minimizing redundancies. The latter idea is implemented with a centralized policy gradient method, which shows promise when evaluated empirically against systems of identical agents, as well as synergy with batch RL techniques that can exploit data diversity. Finally, we provide an original concentration analysis that shows faster rates for specialized parallel sampling distributions, which supports our methodology and may be of independent interest.

Feeders play a crucial role in efficiently transporting materials, parts, and components to work areas in manufacturing industries. They employ various technologies and mechanisms tailored to the specific requirements of the manufacturing process. With the growing demand for flexibility and technology across diverse applications, feeders are evolving into smarter systems, thanks to the integration of industrial robots into their architecture. The challenges associated with grasping tasks, such as object variability, environmental dynamics, and real-time adaptability, underscore the need for advanced cognitive capabilities. Consequently, the application of Reinforcement Learning (RL) becomes instrumental in enabling robots to learn and adapt their grasping strategies autonomously. When combined with Deep Learning (DL), RL techniques drive robots to refine their grasping actions through iterative experiences, ultimately enhancing overall performance and dexterity. The primary goal of this work is to establish a comprehensive pipeline, starting from the 3D model of the object to be grasped and culminating in a trained algorithm capable of managing the hierarchical nature of agents. These agents detect the object to grasp and make decisions regarding the suggested point of picking. This complete workflow facilitates a seamless transition between graspable objects, introducing new flexible capabilities to a smart feeder equipped with an industrial robot. As a result, the integration of RL and DL not only addresses the inherent challenges in grasping tasks but also empowers the feeder system to adapt to a variety of objects dynamically, thereby optimizing efficiency in a manufacturing setting.