Reinforcement learning allows underwater robots to locate and track objects underwater
A research team has shown for the first time that reinforcement learning—i.e., a neural network that learns the best action to perform at each moment based on a series of rewards—allows autonomous vehicles and underwater robots to locate and carefully track marine objects and animals.
A team has shown that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater robots to locate and carefully track marine objects and animals.
In recent years, roboticists have introduced robotic systems that can complete missions in various environments, ranging from the ground to underground, aboveground and underwater settings. While several of these robots can grasp and move objects on the ground, the handling of objects by robotic systems underwater has so far proved…
Reinforcement learning provides a conceptual framework for autonomous agents to learn from experience, analogously to how one might train a pet with treats. But practical applications of reinforcement learning are often far from natural: instead of using RL to learn through trial and error by actually attempting the desired task,…