Is reinforcement learning just trial and error?
While trial and error is a fundamental aspect of reinforcement learning, it is not merely random guessing. My work emphasizes the systematic process of learning from the consequences of actions. Agents learn to associate states with expected future rewards, allowing them to make informed decisions rather than just stumbling upon success. The algorithms are designed to explore the environment effectively and exploit learned knowledge to optimize behavior over time.
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