Reinforcement Learning

Definition of Reinforcement Learning as it relates to Science, Mathematics, Theoretical Computer Science

Reinforcement Learning is an area of theoretical computer science, nested within Science and Mathematics, that focuses on algorithms which learn optimal behavior through trial-and-error interaction with their environment. It's about agents making decisions to achieve specific goals by balancing exploration (trying out new actions) and exploitation (choosing the best action based on current knowledge). The learning process involves feedback in form of rewards or penalties, shaping the agent's future behavior towards maximizing cumulative reward. Techniques include dynamic programming, Monte Carlo methods, temporal difference learning, and Q-learning among others. Reinforcement Learning finds applications in various fields such as robotics, navigation, game playing, resource management, and more.

Note
  • [ReinforcementLearning.org] Machine Intelligence | Personal site of Cosmo Harrigan.
  • [mlrl.org] Machine Learning Reinforcement Learning – Teaching material
  • [rldm.org] RLDM | The Multi-disciplinary Conference on Reinforcement Learning and Decision Making
  • [rlai.net] Reinforcement Learning and Artificial Intelligence, worldwide
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