Glossary

Reinforcement Learning Theory is a field of machine learning. It determines the actions of agents to maximize rewards. It has roots in psychology and utilizes dynamic programming, Monte Carlo methods, and temporal difference learning.

The React Agent Model is a superior framework enhancing large language model’s reasoning abilities, promoting effective comprehension, evaluation, and communication for better information processing.

Reinforcement learning is a potent branch of machine learning, where software agents determine the ideal behavior within a context to maximize cumulative rewards. It encapsulates learning via trial and error, and interaction with its environment, which is instrumental in discovering the most rewarding actions.

The Recurrent Neural Network (Rnn) is an artificial neural network that excels in identifying patterns in sequential data like text, genomes, & spoken words.

Helping artificial intelligence (AI) make decisions, a reasoning system is software that uses logical deduction and induction to draw conclusions from existing knowledge.

Region Connection Calculus is a qualitative spatial representation system, allowing for intricate reasoning about the relationships between different regions in a Euclidean or topological space.