Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
Traffic congestion, fuel consumption, and emissions also offer quantifiable performance indicators, making mobility uniquely ...
A quadruped robot uses deep reinforcement learning to master walking on varied terrains, demonstrating energy-efficient and ...
This multi-objective setup encourages natural walking behavior rather than rigid or inefficient movement. A four-stage ...
Using a bunch of carrots to train a pony and rider. (Photo by: Education Images/Universal Images Group via Getty Images) Andrew Barto and Richard Sutton are the recipients of the Turing Award for ...
This article is published by AllBusiness.com, a partner of TIME. What is "Reinforcement Learning"? Reinforcement Learning (RL) is a type of machine learning where a model learns to make decisions by ...
Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...