Reinforcement Learning in Action - Easy Animation

Welcome to the basics of reinforcement learning! Today we'll explore how agents learn through trial and error.Let's start with a simple environment - a grid world where our agent needs to find a reward.Meet our agent, a curious robot that starts with no knowledge of its environment.The environment is the world our agent interacts with - in this case, a simple grid.The agent's goal is to reach this reward star, which provides positive feedback.The agent can take four possible actions: moving up, right, down, or left.Initially, the agent makes random moves, not knowing which actions lead to the reward.The agent receives feedback for each action. Moving closer to the goal gives positive rewards.While moving away from the goal results in negative feedback.This creates a continuous learning cycle: the agent observes its state, chooses an action, receives feedback, and updates its knowledge.In the next section, we'll see how the agent uses this feedback to learn and improve its decision-making.As our agent explores the environment, it learns the value of different actions in each state.Initially, the agent explores randomly, assigning Q-values to each action it takes.As the agent learns, it updates the Q-values based on the rewards it receives.After learning, the agent starts making better decisions, following paths with higher Q-values.The value function shows the expected future rewards for each state, represented by color intensity.With the learned value function, the agent can now consistently choose actions that lead to the goal.