Reinforcement Learning (RL) is a subfield of machine learning where an agent learns to make sequential decisions through interactions, optimizing strategies by maximizing cumulative rewards.
The agent utilizes a policy for decision-making, receiving rewards based on actions taken, and continually refines this policy to find an optimal strategy through experiential feedback.
Currently, RL is prominently applied across various sectors, enhancing decision-making and performance in games, robotics, natural language processing, and numerous other fields, reflecting its versatility.
RL’s classification into categories, such as Value-based RL, illustrates methodologies for learning action values, providing insights into how agents optimize their decision-making processes.
#reinforcement-learning #machine-learning #sequential-decision-making #optimization #application-areas
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