Q-Learning is a reinforcement learning algorithm used for training an agent to make optimal decisions in a Markov Decision Process (MDP). It is a model-free approach that learns through trial and error, without requiring explicit knowledge of the environment. Q-Learning aims to find the optimal action-selection policy for an agent to maximize cumulative rewards.
Here's a high-level overview of how Q-Learning works:
MDP and Q-Values: In an MDP, an agent interacts with an environment by taking actions and receiving rewards. The agent's goal is to learn an optimal policy that maximizes the long-term cumulative reward. The Q-Value represents the expected cumulative reward an agent can achieve by taking a particular action in a specific state.
Q-Table: Q-Learning utilizes a Q-Table, which is a lookup table that stores the Q-Values for all state-action pairs. Initially, the Q-Table is initialized randomly or with arbitrary values.
Exploration and Exploitation: The agent explores the environment by selecting actions based on an exploration-exploitation trade-off. Initially, the agent favors exploration to discover new actions and states. As the agent learns more, it starts exploiting the learned knowledge to choose actions that maximize the expected reward.
Q-Value Update: The agent updates the Q-Values based on the rewards received and the Q-Values of the next state. The update is performed using the Bellman equation, which expresses the optimal Q-Value as the immediate reward plus the maximum Q-Value of the next state.
Learning Rate and Discount Factor: Q-Learning employs a learning rate (alpha) and a discount factor (gamma). The learning rate determines the weight given to new information, while the discount factor balances immediate and future rewards. These parameters influence the rate at which the agent learns and the importance it assigns to immediate versus long-term rewards.
Exploration Decay: To shift the agent's focus from exploration to exploitation over time, an exploration rate (epsilon) is often used. The exploration rate starts high to encourage exploration and gradually decays as the agent becomes more knowledgeable.
Training and Convergence: The agent iteratively interacts with the environment, updating the Q-Values based on the observed rewards and optimizing its policy. The process continues until the agent's Q-Values converge to the optimal values, reflecting the best policy for action selection in each state.
Q-Learning has been successfully applied in various domains, including robotics, game playing, and control systems, where optimal decision-making based on feedback and rewards is crucial. It is a fundamental algorithm in the field of reinforcement learning, enabling agents to learn optimal policies in complex environments without requiring prior knowledge.