Evaluating the advantages and potential drawbacks of shielding as a method for safe RL. Bettina Könighofer is an assistant ...
For a long time, the core idea in reinforcement learning (RL) was that AI agents should learn every new task from scratch, like a blank slate. This "tabula rasa" approach led to amazing achievements, ...
In recent years, the field of robotics has undergone significant transformation, driven increasingly by advances in brain-inspired and neurally grounded ...
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DeepMind introduces AI agent that learns to complete various tasks in a scalable world model
Over the past decade, deep learning has transformed how artificial intelligence (AI) agents perceive and act in digital ...
In the 1980s, Andrew Barto and Rich Sutton were considered eccentric devotees to an elegant but ultimately doomed idea—having machines learn, as humans and animals do, from experience. Decades on, ...
Reinforcement learning is a subfield of machine learning concerned with how an intelligent agent can learn through trial and error to make optimal decisions in its ...
The UC Berkeley crew has now shown the value of AI-based optimization work by having OpenEvolve work out a more efficient approach to load balancing across GPUs handling LLM inference.
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