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Curiosity-driven reinforcement learning is a subfield of reinforcement learning that incorporates intrinsic motivation mechanisms, encouraging agents to explore their environment beyond external rewards. This approach aims to improve learning efficiency in complex or sparse reward settings by fostering exploration through curiosity.
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Strong artificial intelligence (Strong AI) refers to a theoretical form of artificial intelligence that possesses human-like cognitive abilities, enabling it to understand, learn, and apply knowledge in a manner indistinguishable from human intelligence. Unlike narrow AI, Strong AI aims to achieve general intelligence capable of reasoning, problem solving, and consciousness.
Domain randomization is a technique used in machine learning and robotics to improve the robustness of models by training them on a diverse set of simulated environments.
XNLI is a dataset designed for natural language inference tasks, providing multilingual support and evaluation metrics for model performance.
The Decision diffuser is a conceptual tool used to enhance decision-making processes by integrating diverse perspectives and information.
Transfer learning in deep neural networks is a technique where a model developed for one task is reused for a different but related task, enhancing efficiency and performance.
Transformer-XL is an advanced neural network architecture that extends the Transformer model by introducing recurrence and segment-level recurrence mechanisms to better capture long-range dependencies in sequential data.
A Hopfield network is a form of recurrent artificial neural network that serves as content-addressable (associative) memory, enabling the retrieval of stored patterns.
Self-supervised learning is a machine learning paradigm that uses unlabeled data to train models, allowing them to learn useful representations without extensive human annotation.