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What is Reinforcement Learning in Machine Learning
Watch: 5.1 All About Reinforcement Learning in Machine Learning by KnowledgeGATE Bytes Reinforcement Learning (RL) matters because it enables machines to learn complex decision-making tasks through trial and error, mimicking how humans and animals adapt to dynamic environments. Unlike traditional machine learning, which relies on labeled data or static models, RL thrives in scenarios where an agent must interact with an environment to maximize cumulative rewards. This framework is critical for solving problems involving sequential decisions, uncertainty, and real-time adaptation-areas where other methods fall short. RL stands out by addressing tasks that require balancing exploration and exploitation, optimizing long-term outcomes, and adapting to changing conditions. For example, robotics applications use RL to teach machines to recover from physical disturbances, like the ANYmal robot learning to stand up after a fall. In autonomous vehicles , RL enables cars to manage unpredictable traffic patterns. These capabilities make RL indispensable in environments where pre-programmed solutions are impractical.
