Machine Learning Course · Lesson 5
Reinforcement Learning
Learn how an agent interacts with an environment, takes actions, receives rewards and improves a policy. Explore the trade-offs and apply the ideas to a warehouse-routing challenge.
What Is Reinforcement Learning?
Understand learning through actions, feedback and rewards.
Understand the concept
Reinforcement Learning (RL) is a Machine Learning approach in which an agent interacts with an environment, takes actions and receives feedback such as rewards or penalties. Across repeated interactions, it can learn a strategy that aims to maximise cumulative reward. The feedback does not usually tell the agent the single correct action for every situation.
Business connection
A warehouse robot might learn routes that deliver items efficiently while avoiding blocked aisles. A simulation can be used to explore behaviour before any real-world deployment.
INTERACTIVE VISUAL
Explore the learning loop
Select each stage to follow an agent as it observes a situation, acts and receives feedback.
Observe
The agent receives information about the current situation.
Concept 1 visual · Interactive educational simulation
INTERACTIVE ACTIVITY
Identify the learning setup
A simulated warehouse robot tries different routes and receives a reward for fast, safe deliveries. Which approach is being used?