Reinforcement Learning Fundamentals
Reinforcement learning trains an agent through interaction. Actions influence an environment, rewards communicate outcomes, and the agent learns a policy for choosing future actions.
Learning objectives
What you will be able to do
- Explain reinforcement learning fundamentals accurately in your own words.
- Recognise how the concept appears in real AI products and professional work.
- Identify an important limitation, risk or evaluation requirement.
- Apply the concept through a practical activity and knowledge check.
Core explanation
Build the right mental model
Unlike supervised learning, the agent is not shown the correct action at every step. It explores, observes consequences and attempts to maximise cumulative reward. This makes reinforcement learning useful for sequential decisions.
Reward design is difficult. A poorly specified reward can produce behaviour that technically scores well while violating the designer’s intention. Safe exploration is also essential when actions can affect people, money or physical systems.
Reinforcement learning powers research in games, robotics, resource allocation and control. Human-feedback techniques also use preference signals to shape generative-model behaviour.
Agent
The decision-maker that selects actions.
Environment
The world the agent observes and influences.
Reward
A numerical signal indicating immediate outcome quality.
Policy
The strategy used to choose an action from a state.
Adaptive traffic signals
An agent changes signal timing and receives reward for reduced waiting time.
Apply your learning
Practical activity
- Define an agent, environment, three actions and a reward for a simple problem.
- Then identify one way the agent could exploit the reward incorrectly.
Write your answers in a learning journal. The value comes from connecting the concept to your own profession.
Knowledge check
Test your understanding
Q1What does a policy represent?
Answer: The agent’s strategy for selecting actions in different states.
Q2Why is reward design a safety issue?
Answer: The agent may optimise the literal reward in unintended or harmful ways.
If you cannot explain the answer without reading it, revisit the core explanation and example before continuing.
Continue with authoritative sources
Important industry resources
These links lead to official organisations, industry laboratories or established open-source learning projects.