Global optimization plays a critical role in addressing complex real-life challenges across various fields. In engineering, it is applied to structural design optimization, where minimizing weight or material use while ensuring durability is essential for cost-effective and safe construction. In financial services, portfolio optimization requires balancing risk and return by finding the global minimum or maximum in investment strategies. In logistics and transportation, global optimization is crucial for solving routing problems such as determining the shortest path or optimizing delivery routes which leads to significant cost savings and improved efficiency. Similarly, in energy systems, global optimization is key to managing and distributing power more efficiently, reducing operational costs, and optimizing renewable energy usage. In machine learning, the need for global optimization is especially pronounced. The performance of models often depends on the ability to minimize complex, non-convex loss functions. While traditional methods like gradient descent are effective in many cases, they frequently encounter the problem of getting trapped in local minima, which can hinder the model's overall performance. This is particularly relevant in tasks that require complex models where the optimization landscape is highly non-linear and fraught with local minima.

When does a robot planner need a map? Reactive methods that use only the robot's current sensor data and local information are fast and flexible, but prone to getting stuck in local minima. Is there a middle-ground between fully reactive methods and map-based path planners? In this paper, we investigate feed forward and recurrent networks to augment a purely reactive sensor-based planner, which should give the robot geometric intuition about how to escape local minima. We train on a large number of extremely cluttered worlds auto-generated from primitive shapes, and show that our system zero-shot transfers to real 3D man-made environments, and can handle up to 30% sensor noise without degeneration of performance. We also offer a discussion of what role network memory plays in our final system, and what insights can be drawn about the nature of reactive vs. map-based navigation.

There are three existing connection::;t models in which network states are assigned a computational energy.

There are three existing connection::;t models in which network states are assigned a computational energy.

There are three existing connection::;t models in which network states are assigned a computational energy.