Q-learning: the algorithm of reinforcement learning

When an animal is trained, it is rewarded for each correct answer. The same reward-based process can be applied to software so that the program effectively performs the required tasks. Reinforcement learning is a machine learning technique used for the development of artificial intelligence that allows machines to be trained with the help of certain algorithms such as Q-learning, a very effective algorithm.

Through reinforcement learning an agent can learn to perform optimal actions in a given environment. The return of a reward as an effect of the action is the ‘reinforcement’ of this learning modality, which is very different from other methods used, such as supervised learning. In fact, in this case, the training data act as responses to the solution, and the model already includes the correct responses. 

In contrast, in reinforcement learning the algorithm does not include responses, but rather the agents decide on the actions to be performed based on the task. The machine learns from experience without seeking help from training data and must evaluate the best among a set of actions, considering the best reward resulting from each of them for the specific environment.

What is Q-learning and how does it work?

Q-learning is a reinforcement learning algorithm based on the idea of trial-and-error learning. Its main objective is to discover the optimal strategy that guides the agent’s actions to maximize the expected value of future rewards. The agent learns to estimate the value of each possible action in a specific state. 

These values are stored in a table known as Q-table, a map that connects each state with all possible actions and their respective utility values, i.e., the expected gain for the agent when performing a certain action in a specific state. The agent then uses these utility values to select optimal actions based on each situation.

Q-learning follows an iterative learning process and is based on the Bellman equation, which expresses the optimal value of a policy as the sum of the immediate rewards and the expected value of future rewards. This equation is fundamental to calculate the utility values in the Q-table.

The Q-learning process can be divided into several phases:

• Initialization of the Q-table: initially, all values are set to zero or to a random value. This represents the agent’s initial ignorance of the quality of the actions.
• Environment exploration: the agent begins to explore the environment and perform random actions. This stage is known as ‘exploration’ and is fundamental to collect data about the environment.
• Q-table update: after performing an action in a specific state, the agent receives a reward and observes the new state it is in. It then uses this information to update the value in the Q-table. The update is based on the Bellman equation and aims to improve the estimation of utility values.
• Selection of optimal actions: once the agent has explored the environment sufficiently and updated the Q-table, it can start selecting optimal actions. These actions are chosen based on the highest values, as they represent the actions that maximize the expected reward.
• Continuous learning: The learning process continues as the agent continues to interact with the environment. Each new experience helps refine the agent’s knowledge and improve its ability to make optimal decisions.

Applications

Q-learning is widely used in a variety of applications.

• Games and Robotics: this paper describes an agent capable of learning to play ‘Stratego’, a game of considerable complexity because it requires the ability to make decisions in the face of imprecise information. The, there is a noteworthy robot, created by researchers at the University of California, which learned in a very short time to move autonomously without prior training.
• Recommendation systems: agents learn from user data and suggest products or content based on individual preferences, thus maximizing user satisfaction.
• Resource management: in resource management applications, such as air and citizen traffic control or supply chain management, Q-learning is used to optimize decisions and mitigate problems such as congestion and delays.
• Autonomous systems: self-driving cars, drones and industrial robots benefit from Q-learning to learn how to navigate complex environments and perform specific tasks.
• Industrial Control Systems: in the context of industrial automation, Q-learning can be used to improve process control, optimizing efficiency and reducing costs.

Despite its numerous applications and successes, there are still some challenges and limitations regarding Q-learning

• Very large space for states and actions: in environments with a large number of possible states and actions, the size of the Q-table becomes very large, which makes the learning process inefficient. To address this problem, approaches such as Deep Q-learning have been developed, which combines Q-learning with neural networks. 
• Exploration issues: determining when to explore new actions and when to exploit existing knowledge is a crucial challenge. A policy that is too conservative can lead to suboptimal results, while a policy that is too exploratory can lead to delays in learning.
• Inefficient learning with low rewards: in environments where rewards are low or delayed, Q-learning can take a long time to learn an optimal policy. This is known as the ‘credit allocation’ problem.
• Sensitive parameters: Q-learning requires tuning parameters such as the learning rate and the discount rate, which can significantly influence the performance of the algorithm.

These machine learning modalities show promising capabilities to match and, in some respects, surpass the human mind. However, they still fall far short of the flexibility of human intelligence.