Reinforcement learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment. Instead of being told what to do, the agent takes actions and receives feedback in the form of rewards or penalties. The goal is to maximize cumulative rewards over time by discovering an optimal strategy, known as a policy. RL is inspired by trial-and-error learning in humans and animals, where behavior improves through experience. It’s particularly useful for tasks with sequential decision-making, such as robotics, game playing, and autonomous systems, where actions impact not only immediate rewards but also future outcomes.

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

Supervised learning resembles a structured classroom environment, where explicit feedback is given for each example (e.g., a teacher correcting a student's answers). In contrast, reinforcement learning mirrors experiential learning, where feedback comes as rewards or penalties after actions, guiding behavior toward long-term goals. For instance, a child learning to ride a bike might fall (penalty) or stay balanced (reward), gradually improving through trial and error.

Conditioning is a learning process where an individual forms associations between stimuli or behaviors and their outcomes. It can be divided into two main types:

Classical Conditioning: Involves pairing a neutral stimulus with a meaningful one to elicit a similar response (e.g., Pavlov’s dogs salivating at the sound of a bell).

Operant Conditioning: Involves learning through rewards or punishments, where behaviors are strengthened or weakened based on their consequences (e.g., Thorndike’s Law of Effect).

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.

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:

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

In 2016, AlphaGo stunned the world by defeating Go champion Lee Sedol, proving that AI could outthink humans in one of the most complex games ever. Using deep learning and Monte Carlo Tree Search, it played moves no human dared—showcasing creativity, brilliance, and the unsettling realization that humanity might be screwed.

"Cells that fire together, wire together."

When two neurons in the brain activate at the same time repeatedly, their connection strengthens. This makes it easier and more probable for one to trigger the other in the future.

Imagine practicing a particular brushstroke over and over. Each time, your hand and brain coordinate, and with practice, the connection becomes stronger and the stroke becomes smoother. Similarly, Hebb’s law underpins how practice makes perfect.