Reinforcement learning is a type of machine learning where an agent learns by interacting with its environment. It receives rewards for good actions and penalties for bad ones, gradually improving its strategy through trial and error

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Reinforcement learning is a powerful machine learning paradigm where an agent learns to make decisions by interacting with an environment. Unlike supervised learning which uses labeled examples, or unsupervised learning which finds hidden patterns, reinforcement learning uses a reward system. The agent takes actions in the environment and receives rewards or penalties, gradually improving its strategy through trial and error. The reinforcement learning framework consists of four core components that work together. The agent is the learner and decision maker. The environment is the world the agent interacts with. Actions are the choices available to the agent at each step. States represent the current situation of the environment. These components are interconnected: the agent observes the current state, selects an action, which changes the environment state and generates a reward signal back to the agent. The reward system is the heart of reinforcement learning. Positive rewards encourage good actions that move the agent toward its goal. Negative rewards or penalties discourage bad actions that lead to undesirable outcomes. Neutral rewards provide no feedback. In this grid world example, the agent receives plus ten for reaching the goal, minus five for hitting traps, and zero for empty spaces. The green path shows a good sequence leading to high reward, while the red path demonstrates a bad sequence resulting in penalties. Learning through trial and error is an iterative process that improves over time. Initially, the agent explores randomly, trying different actions to understand the environment. As it gains experience, it begins exploiting known good actions while still exploring new possibilities. The agent continuously updates its policy based on received rewards and penalties. This graph shows how performance typically improves over episodes, with rewards increasing and errors decreasing. Eventually, the agent converges to an optimal or near-optimal strategy, demonstrating successful learning through accumulated experience. Let's walk through a complete reinforcement learning example using maze solving. The agent starts at the entrance marked S and must reach the goal marked G. Initially, the agent explores randomly, trying different directions and often hitting walls or taking inefficient paths. Each action results in a reward: plus one hundred for reaching the goal, minus ten for hitting walls, and minus one for each step to encourage efficiency. After many episodes, the agent learns the optimal policy, taking the shortest path directly to the goal. This demonstrates the complete reinforcement learning cycle from random exploration to optimal strategy through accumulated experience and reward-based learning.