This multi-objective setup encourages natural walking behavior rather than rigid or inefficient movement. A four-stage ...

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 ...

Deepreinforcement learning has disadvantages such as low sample utilization and slow convergence, and thousandsof trial-and-error iterations are required to perform ...

A new machine-learning technique can train and control a reconfigurable soft robot that can dynamically change its shape to complete a task. The researchers also built a simulator that can evaluate ...

FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.

Over the past two decades, humanoid robots have greatly improved their ability to perform functions like grasping objects and using computer vision to detect things since Honda’s release of the ASIMO ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...