In reinforcement learning (RL), an agent is an autonomous entity that learns to make decisions by interacting with an environment. The agent’s goal is to maximize cumulative rewards over time by selecting actions based on its observations. Unlike supervised learning, where the model learns from labeled data, the agent in RL learns through trial and error, receiving feedback in the form of rewards or penalties. For example, in a game like chess, the agent might be an AI player that explores moves (actions), observes the resulting board state (environment), and receives rewards (e.g., +1 for a win, -1 for a loss) to refine its strategy.
The agent’s behavior is governed by a policy, which defines the strategy it uses to choose actions. Policies can be deterministic (directly mapping states to actions) or stochastic (assigning probabilities to actions). The agent also relies on a value function to estimate the long-term value of being in a state or taking an action. For instance, in a maze-solving task, the agent might prioritize paths that historically lead to higher rewards. Additionally, agents often balance exploration (trying new actions to discover better strategies) and exploitation (leveraging known high-reward actions). A classic example is Q-learning, where the agent maintains a table (Q-table) to track the expected rewards of actions in specific states, updating it iteratively as it interacts with the environment.
From a developer’s perspective, building an RL agent typically involves selecting or designing algorithms (e.g., Deep Q-Networks for complex environments), defining reward structures, and configuring exploration strategies. Frameworks like TensorFlow, PyTorch, or libraries such as OpenAI Gym provide tools to simulate environments and train agents. Practical challenges include tuning hyperparameters (e.g., learning rates) and managing computational costs. For example, training a robot to walk in a simulation might involve using Proximal Policy Optimization (PPO) to stabilize learning, while ensuring rewards are shaped to encourage desired behaviors (e.g., forward movement). Effective agents require careful design to translate theoretical concepts into functional, efficient systems.