In reinforcement learning (RL), actions are the decisions an agent makes to interact with its environment. These choices directly influence the environment’s state and determine the feedback (rewards or penalties) the agent receives. Actions are a core component of the RL loop: the agent observes the current state, selects an action based on its policy, and then transitions to a new state while receiving a reward. For example, in a game like Pac-Man, an action might be moving left, right, up, or down to avoid ghosts and collect points. The agent’s goal is to learn a strategy (policy) that maximizes cumulative rewards over time by selecting the most effective actions in different states.
Actions can be discrete or continuous, depending on the problem. Discrete actions are finite and distinct, like choosing between a set of buttons in a video game. Continuous actions involve a range of possible values, such as adjusting the throttle of a self-driving car. For instance, a robot arm grasping an object might use continuous actions to fine-tune motor torques. The type of action space impacts algorithm selection: Q-learning works well for discrete actions, while policy gradient methods like Proximal Policy Optimization (PPO) handle continuous control. Designing the action space requires balancing complexity—too many options can slow learning, while overly simplistic actions may limit the agent’s ability to achieve goals.
The selection of actions is guided by the agent’s policy, which maps states to actions. During training, the agent explores by taking random or uncertain actions to discover high-reward strategies, then exploits known effective actions. Techniques like epsilon-greedy (choosing random actions with probability epsilon) or Boltzmann exploration (probabilistically selecting based on action values) balance this trade-off. For example, in training an RL agent to play chess, early episodes might involve random moves (exploration), but over time, the agent prioritizes moves that lead to checkmate (exploitation). Actions are central to how the agent learns: every choice provides data to refine the policy, making action selection a critical factor in solving RL problems efficiently.