Reinforcement Learning
Reinforcement learning (RL) is a machine learning paradigm in which an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties as feedback, aiming to maximize cumulative reward over time.
Definition
Reinforcement learning (RL) is a machine learning paradigm in which an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties as feedback, aiming to maximize cumulative reward over time.
Overview
Unlike supervised learning, where a model learns from a fixed dataset of labeled input-output pairs, reinforcement learning learns through trial and error. An agent observes the state of an environment, takes an action, and receives a reward signal indicating how good that action was; over many iterations, it updates its strategy (policy) to favor actions that lead to higher long-term reward. This makes RL well suited to sequential decision-making problems, such as game-playing or robotic control, where the consequences of an action may not be apparent until much later. Reinforcement learning has become especially important in modern AI beyond games and robotics: reinforcement learning from human feedback (RLHF) is a key stage in training conversational assistants like the ones behind ChatGPT, where human preference ratings are used as the reward signal to make model outputs more helpful, honest, and aligned with user expectations. Training an RL agent typically involves balancing exploration (trying new actions to discover better strategies) against exploitation (using known good actions), and optimization is often driven by variants of gradient descent applied to the policy or value function. RL remains an active and rapidly evolving area, particularly for training AI agents that plan and act autonomously over multiple steps.
Key Concepts
- Learns through trial-and-error interaction with an environment, not fixed labeled data
- Optimizes for cumulative long-term reward rather than immediate correctness
- Balances exploration of new actions against exploitation of known good ones
- Powers reinforcement learning from human feedback (RLHF) in LLM training
- Well suited to sequential, multi-step decision-making problems
- Used heavily in robotics, game-playing agents, and autonomous systems