What Is Reinforcement Learning?

Reinforcement learning teaches AI through reward and consequence, not labeled examples. Here's what it is, where it shines, and where it doesn't fit.

What reinforcement learning is

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties—improving its strategy through trial and error.

How it differs from supervised learning

	Supervised	Reinforcement
Learns from	Labeled examples	Rewards/penalties
Goal	Predict an answer	Learn a strategy
Feedback	Explicit correct answer	Consequences of actions

See supervised vs unsupervised learning for the other main paradigms.

Where RL shines

RL excels at sequential decision-making:

• Game playing — learning winning strategies.
• Robotics & control — learning to act physically.
• Optimization — dynamic pricing, routing, recommendations.

Where it's harder

RL requires a well-defined reward and lots of interaction—which makes it harder to apply than supervised learning for many business problems, where labeled data is easier to use. RLHF, used to align LLMs, is a specialized application of RL ideas.

Practical takeaway

For most business predictions, supervised learning is the practical choice; reach for RL when the problem is genuinely about sequential decisions with a clear reward—the right-tool discipline.

Why FISTA

FISTA Solutions picks the right learning approach for your problem—supervised, unsupervised, or reinforcement—so you don't over-engineer, through AI enablement, backed by 150+ projects across 12+ countries.

Choosing the right ML approach? Talk to FISTA.