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 ...
Deep Reinforcement Learning (DRL) is a subfield of machine learning that combines neural networks with reinforcement learning techniques to make decisions in complex environments. It has been applied ...
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 ...
In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Autonomous vehicles (AVs) have the potential to transform transportation systems by improving safety, efficiency, accessibility, and comfort. However, developing reliable control policies for AVs to ...
Picture this: a self-driving car smoothly navigating treacherous mountain roads with consecutive hairpin turns – a scenario that would challenge even the most experienced human drivers. This vision is ...
AZoSensors on MSN
Energy-aware protocol cuts power use in green IoT networks
Researchers introduce the EAVM protocol, achieving 17 % lower energy use and 20 % longer network lifetime in IoT systems with advanced virtualization techniques.
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