COMPEL Glossary / reinforcement-learning
Reinforcement Learning
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns by interacting with an environment and receiving rewards or penalties for its actions.
What this means in practice
Unlike supervised learning, there is no dataset of correct answers -- the agent must discover effective strategies through trial and error. RL produced spectacular results in games (AlphaGo, Atari) and is increasingly applied to enterprise optimization problems: dynamic pricing, logistics scheduling, robotic control, and resource allocation. Enterprise RL adoption is growing but less mature than supervised approaches. RL is also foundational to RLHF (Reinforcement Learning from Human Feedback), the technique used to align large language models with human preferences and safety requirements.
Why it matters
Reinforcement learning powers enterprise optimization problems including dynamic pricing, logistics scheduling, robotic control, and resource allocation where trial-and-error learning outperforms traditional approaches. It is also foundational to RLHF, the technique used to align language models with human preferences. Understanding RL helps leaders assess which optimization problems are suited to this approach and what infrastructure investments are required.
How COMPEL uses it
During the Model stage, RL feasibility is assessed as part of the Technology pillar's use case evaluation, with emphasis on environment simulation requirements and reward function design. The Calibrate stage evaluates whether the organization has the data infrastructure and compute capacity for RL workloads. The Evaluate stage monitors RL system performance, and the Governance pillar ensures reward functions align with organizational values.
Related Terms
Other glossary terms mentioned in this entry's definition and context.