In reinforcement learning (RL), actions refer to the choices or decisions that an agent can make in an environment. These actions are critical because they determine the next state of the environment and, ultimately, the rewards that the agent receives. The agent learns to select actions based on its experiences, aiming to maximize the total reward over time. Actions can be discrete, like choosing to move left or right, or continuous, such as adjusting the speed of a robot. The specific set of actions available to an agent depends on the design of the environment and the problem being solved.
For instance, in a game like chess, the actions are the possible moves a player can make with their pieces. Each move has consequences that affect the state of the game, potentially leading to victory or defeat. The agent, whether it is a human player or a program, learns from various outcomes associated with its actions. This involves exploring different strategies and evaluating which moves yield the best results in terms of winning the game. In contrast, in a robotic vacuum cleaner, the actions might include moving forward, turning, or stopping. The machine evaluates these actions based on how effectively they clean a space while avoiding obstacles.
In reinforcement learning, there is usually a balance between exploration and exploitation when selecting actions. Exploration involves trying out new actions to discover their effects, while exploitation focuses on using known actions that provide high rewards. For example, if the RL agent has learned that moving left in a maze leads to a reward, it might favor that action. However, if the agent never explores other actions, it may miss discovering a shortcut that leads to an even higher reward. Therefore, managing this balance is essential for effective learning in an RL framework.