An Intuitive Introduction to Reinforcement Learning, Part I

Reinforcement learning involves an agent interacting with an environment, receiving rewards for actions that lead to desirable outcomes. The environment can be represented as a Markov decision process (MDP), where the agent's actions and the environment's response are determined by probabilities. The optimal course of action is determined by predicting future rewards, known as the state-value or action-value function. Q-learning is an algorithm that iteratively updates the action-value function based on experience, allowing the agent to learn the optimal actions for each state. In the "Frozen Lake" environment, the agent navigates a grid while avoiding holes, with the goal of reaching the end position. The action-value function for the "Frozen Lake" environment is initially set to zero for all states. As the agent explores the environment, the action-value function is updated based on the rewards received for each action-state pair. The agent selects actions based on the highest action-value, gradually learning the optimal path to the end position. The transitional probabilities in the "Frozen Lake" environment introduce randomness, making it more challenging for the agent to determine the optimal path. Q-learning allows the agent to adapt to the stochastic nature of the environment and learn the optimal policy, maximizing the expected long-term reward.