A deep reinforcement learning framework optimizes silicon-based photonic crystal fiber modulators, achieving ultra-low ...
Learning from potential disinformation introduces specific cognitive biases, causing individuals to systematically deviate from an idealized Bayesian updating strategy.
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Multi-agent reinforcement learning driving smart factory agility
At the core of Industry 4.0, the smart factory integrates automation, mass customization, and self-organization into a highly ...
When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill. Researchers at MIT, the ...
MPC, a well-known control methodology that exploits a prediction model to predict the future behaviour of the environment and compute the optimal action and RL, a Machine Learning paradigm that showed ...
This paper investigates the potential of the intrinsically motivated reinforcement learning (IMRL) approach for robotic drumming. For this purpose, we implemented an IMRL-based algorithm for a ...
Abstract: This article dedicates to investigating a methodology for enhancing adaptability to environmental changes of reinforcement learning (RL) techniques with data efficiency, by which a joint ...
Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...
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