👉 Reinforcement Learning (RL) is a subfield of machine learning where an agent learns to make decisions by interacting with an environment to maximize cumulative rewards. The mathematical foundation of RL involves concepts from optimization, probability theory, and stochastic processes. Key components include the state space (S), action space (A), transition probabilities (P), and reward function (R). The agent aims to learn a policy π, which maps states to actions, that maximizes the expected cumulative reward over time, often formalized as the expected return or cumulative reward. This is typically achieved using algorithms like Q-learning, which updates action-value functions (Q-values) based on the Bellman equation, or policy gradient methods, which directly optimize the policy parameters. The exploration-exploitation trade-off is managed through strategies like epsilon-greedy or entropy regularization, ensuring the agent balances trying new actions with exploiting known good actions.
