Reinforcement Learning in Dynamic Environments: Applications and Limitations
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Abstract
When it comes to handling sequential decision-making challenges, especially in uncertain and dynamic situations, Reinforcement Learning (RL) has become a potent AI paradigm. Reinforcement learning (RL) allows agents to learn optimal behaviors through interaction with the environment by getting feedback in the form of rewards or penalties, unlike standard supervised learning systems. For situations that change over time and necessitate flexible decision-making approaches, RL is an excellent choice. the theories and methods of reinforcement learning in dynamic settings, with an emphasis on how agents figure out how to get the most out of their rewards in the long run regardless of how the circumstances change. It highlights the strengths of important algorithms in dealing with complicated, high-dimensional state spaces, including Q-learning, Deep Q-Networks (DQN), and policy gradient approaches. In addition to theoretical considerations, the paper delves into practical uses of RL in fields where flexibility and ongoing education are crucial, such as robots, autonomous cars, gaming, resource management, and financial trading.
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