Q-Learning

Q-Learning is a reinforcement learning algorithm that helps an agent learn the best actions to take in different states of an environment. It builds a Q-table to represent the expected cumulative rewards for actions in specific states, allowing the agent to make informed decisions and improve its policy through exploration and exploitation of the environment.

It works well if the state space is small. Otherwise it becomes computationally ineffective. An alternative is to use a Deep Q-Learning algorithm to approximate the Q-function with a deep neural network.

Algorithm

See it in action here.

Pros

• Model-Free: Q-Learning does not require a model of the environment, making it suitable for situations where the dynamics of the environment are unknown.
• Versatility: It can be applied to a wide range of problems, including complex and non-deterministic environments.
• Exploration-Exploitation: Q-Learning incorporates a balance between exploration of new actions and exploitation of known actions, helping to discover optimal policies.
• Asynchronous Learning: Q-Learning can update Q-values asynchronously, making it suitable for online learning and dynamic environments.

Cons

• Computational Complexity: In environments with a large state or action space, maintaining and updating the Q-table can be computationally expensive.
• Continuous State Spaces: Q-Learning may face challenges when dealing with continuous state spaces, requiring discretization techniques.
• Exploration Challenges: It may struggle in scenarios where exploration is difficult or expensive, leading to suboptimal policies.
• Delayed Rewards: Q-Learning might have difficulties learning from delayed or sparse rewards, impacting the efficiency of learning.