From the course: Agentic AI Fundamentals: Architectures, Frameworks, and Applications
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Reinforcement learning and Q-learning
From the course: Agentic AI Fundamentals: Architectures, Frameworks, and Applications
Reinforcement learning and Q-learning
- Reinforcement learning algorithms are instrumental in developing machine learning and agentic AI. These algorithms find their place in numerous practical scenarios including autonomous driving, where agents learn to navigate roads, or troubleshooting a jet engine in flight, automatically finding the best and safest outcome, Reinforcement learning is all about learning by interacting with an environment. The agent takes action in different situations, also known as states, and receives feedback in the form of rewards or punishments. Agents try to maximize rewards much like people do when we get rewards for good conduct or good grades in school. The agent understands that it's not desirable to get a penalty and attempts to figure out how to avoid it in the future. Q-learning is a specific type of reinforcement learning algorithm. It uses a table, a Q-table, to keep track of the best actions to take in each situation. The agent updates this table based on experiences, gradually…
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