Mathematical Foundations of Reinforcement Learning

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Publisher ‏ : ‎ Springer; 2024th edition (February 22, 2025)
Language ‏ : ‎ English
ISBN-10 ‏ : ‎ 9819739438
ISBN-13 ‏ : ‎ 978-9819739431
Item Weight ‏ : ‎ 1.74 pounds

Reinforcement learning is a powerful machine learning technique that enables an agent to learn how to make decisions by interacting with an environment and receiving rewards or punishments based on its actions. At the core of reinforcement learning lies a strong foundation in mathematics, which helps to formalize the learning process and optimize the agent’s decision-making.

One key mathematical concept in reinforcement learning is the Markov decision process (MDP), which models the environment as a set of states, actions, transition probabilities, and rewards. MDPs provide a framework for formalizing the sequential decision-making process and finding an optimal policy for the agent to follow.

Another important mathematical tool in reinforcement learning is the concept of value functions, which quantify the expected future rewards that an agent can achieve from a given state or action. By optimizing these value functions, the agent can learn to make decisions that maximize its long-term rewards.

Additionally, reinforcement learning often involves the use of optimization techniques such as dynamic programming, Monte Carlo methods, and temporal difference learning. These methods help to update the agent’s policy based on its experiences and improve its decision-making over time.

Overall, a strong understanding of mathematical concepts such as MDPs, value functions, and optimization techniques is essential for developing and implementing effective reinforcement learning algorithms. By leveraging these mathematical foundations, researchers and practitioners can push the boundaries of what is possible in artificial intelligence and autonomous decision-making.
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