Reinforcement Learning algorithms aim to find optimal strategies for agents to interact with environments, learning from trial and error. They typically involve iteratively updating policies or value functions based on observed rewards and transitions, balancing exploration and exploitation to maximize long-term rewards. RL algorithms encompass various approaches such as Passive RL like direct evaluation and temporal difference learning and Active RL such as Q-Learning
