A reinforcement learning-based four-legged robotic goalkeeper
Researchers at the Hybrid Robotics Group at UC Berkeley, Simon Fraser University and Georgia Institute of Technology have recently created a reinforcement learning model that allows a quadrupedal robot to efficiently play soccer in the role of goalkeeper. The model introduced in a paper pre-published on arXiv, improves the robot’s skills over time, through a trial-and-error process.
Policy gradient algorithms have driven many recent advancements in language model reasoning. An appealing property is their ability to learn from exploration on their own trajectories, a process crucial for fostering diverse and creative solutions. As we show in this paper, many policy gradient algorithms naturally reduce the entropy—and thus…
Reinforcement learning (RL) is a subfield of machine learning where an agent learns to make decisions by interacting with its environment rather than relying solely on pre-existing data. It is an area that blends trial-and-error learning with feedback from actions to improve future performance. In this blog, we will explore…