DRL is a type of machine learning where an agent learns to make decisions by trial and error, guided by rewards or penalties, using deep neural networks. Unlike traditional methods, which struggle with complex environments, DRL allows machines to learn directly from raw data, like images or game screens. The neural network helps the agent recognize patterns and improve its decisions over time. DRL has achieved impressive results in tasks like playing video games (e.g., Atari, AlphaGo), controlling robots, and developing self-driving cars, making it a powerful tool for solving real-world problems involving sequential decision-making

Experiential learning, Unsupervised learning, Supervised learning, Classifiers & Machine Learning ...

Q-learning is a model-free reinforcement learning algorithm that enables an agent to learn an optimal policy for decision-making. It works by estimating the Q-values (action-value function), which represent the expected cumulative reward for taking an action in a given state and following the best future actions. The agent updates Q-values iteratively using the formula:

In machines, reinforcement learning (RL) is implemented using an agent-environment framework. The agent interacts with an environment by taking actions based on a policy (a strategy for decision-making). The environment provides feedback in the form of rewards or penalties, guiding the agent to improve its actions. Key components include a reward function to evaluate outcomes, a value function to estimate long-term benefits of actions, and exploration strategies to balance learning new behaviors versus exploiting known rewards.