Ever wondered how AI actually learns to make decisions? In my latest video, I break down Reinforcement Learning (RL) Agents — the core of how AI interacts with its environment, learns from feedback, and improves over time.
🔍 You’ll learn: ✅ What an RL Agent is ✅ How the Agent–Environment Loop works ✅ Key concepts like state, action, reward ✅ Real-world examples & applications
Whether you're an AI beginner or a curious tech enthusiast, this video makes RL concepts simple and clear.
📺 Watch now: https://youtu.be/dmd_ljw03JM
#ReinforcementLearning #ArtificialIntelligence #MachineLearning #AI #DeepLearning #DataScience #AIExplained
Rahul Jain Nice breakdown — RL often feels abstract until you see the agent–environment loop in action. The real-world examples you’ve included make the concepts click for beginners and practitioners alike.
Dear Rahul Jain ,
Reinforcement learning (RL) agents are software entities that learn to make decisions by interacting with an environment, aiming to maximize a reward signal. They operate through trial and error, receiving feedback in the form of rewards or penalties for their actions. Unlike supervised learning, RL agents don't rely on labeled data; instead, they learn optimal policies by exploring and exploiting the environment.
In the realm of machine learning, Reinforcement Learning (RL) stands out as a unique paradigm that mimics how humans and animals learn through trial and error. Unlike supervised learning, where models are trained on labeled data, RL teaches an agent to interact with an environment and learn from the consequences of its actions.
https://medium.com/@bhatadithya54764118/day-62-reinforcement-learning-basics-agent-environment-rewards-306b8e7e555c
Regards,
Shafagat
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