Reinforcement Learning
Expanded definition
Reinforcement Learning (RL) is an area of machine learning where an agent learns to make decisions by interacting with an environment to achieve a specific goal. The agent receives feedback in the form of rewards or penalties based on its actions, which guides it in improving its decision-making over time. The aim is to develop a policy that maximizes the expected cumulative reward, resulting in optimal behavior in various situations.
Related terms
Explore adjacent ideas in the knowledge graph.
Reinforcement Learning FAQ
What is Reinforcement Learning?
A type of machine learning focused on teaching agents to make decisions by maximizing cumulative rewards.
How is Reinforcement Learning used in AI systems?
Reinforcement Learning (RL) is an area of machine learning where an agent learns to make decisions by interacting with an environment to achieve a specific goal. The agent receives feedback in the form of rewards or penalties based on its actions, which guides it in improving its decision-making over time. The aim is to develop a policy that maximizes the expected cumulative reward, resulting in...
Related
Comparisons, tools, and models that connect to this idea.