Reinforcement Learning definition
Reinforcement learning (RL) is a type of machine learning in which an agent learns to make decisions by acting in an environment and receiving rewards or penalties. Through trial and error it learns a policy, a strategy that maximizes total reward over time. RL powers game-playing systems, robotics control and the tuning of language models.
How does reinforcement learning work?
Reinforcement learning has five parts: an agent, an environment, states, actions and rewards. At each step the agent observes the current state, chooses an action and receives a reward plus a new state. Over many episodes it learns which actions lead to the highest cumulative reward, not just the best immediate payoff. A chess program that sacrifices a piece to win the game later is a classic illustration of that long-term thinking.
The core tension is exploration versus exploitation. An agent that always repeats its best-known action may never discover a better one, while an agent that explores too much wastes time on poor choices. Algorithms balance the two with strategies such as epsilon-greedy, where the agent tries a random action a small fraction of the time, or with uncertainty-driven exploration that favors actions it knows little about.
Key reinforcement learning methods
Libraries such as Gymnasium, the maintained successor to OpenAI Gym, provide standard environments for testing agents, while Stable-Baselines3 and Ray RLlib offer tested implementations of common algorithms. Teams rarely need to write these methods from scratch, and most of the effort goes into environment and reward design.
- Q-learning and Deep Q-Networks (DQN): learn the value of each action in each state.
- Policy gradient methods such as PPO: adjust the policy directly to raise expected reward.
- Actor-critic methods: pair a policy (the actor) with a value estimate (the critic) for more stable learning.
- Multi-armed and contextual bandits: a simplified form used for offers, pricing tests and recommendations.
- Model-based RL: learns a model of the environment and plans ahead with it.
Reinforcement learning vs supervised learning
Supervised learning is told the correct answer for every example. Reinforcement learning is only told how good an outcome was, often long after the action that caused it. That makes RL suited to sequential decisions where nobody can label the perfect move, such as controlling a robot arm or scheduling a delivery fleet. It also makes RL slower and less stable to train, and highly sensitive to how the reward is designed.
Real-world uses, including RLHF
DeepMind's AlphaGo used reinforcement learning to beat a top professional Go player in 2016, and RL has since been applied to data center cooling, chip floorplanning, warehouse robotics and ad bidding. One of its most widespread uses is reinforcement learning from human feedback (RLHF), where people rank model answers, a reward model learns those preferences, and a language model is tuned to produce responses people rate higher.
For most businesses, RL is a specialist tool. It needs a simulator or a safe way to experiment, because learning by trial and error on live customers or machines can be costly. Contextual bandits for choosing offers or layouts are usually the practical entry point. Nexzem typically recommends supervised or bandit approaches first and moves to full reinforcement learning only when a reliable simulator or a safe testing environment exists.