Reinforcement Learning
A machine learning method in which an agent learns decision-making through rewards and penalties.
What Is Reinforcement Learning
Reinforcement learning is a machine learning paradigm where an agent learns behaviour by interacting with an environment. Instead of being shown explicit correct answers, the agent receives reward or penalty signals and learns a policy that maximises cumulative reward over time.
How Reinforcement Learning Works
The agent repeatedly selects actions, observes outcomes, and updates its strategy based on feedback from the environment.
- observe the current state of the environment
- choose an action according to a policy
- receive a reward signal and next state
- update value estimates or policy parameters
- balance exploration and exploitation over many episodes
Key Concepts: Agent, Environment, Reward
The agent is the learner or decision-maker. The environment is the system it interacts with. The reward is a numerical signal indicating how good or bad an action was in a given context. These three concepts define the feedback loop that drives learning.
Role of Reinforcement Learning in AI Systems
Reinforcement learning is used when AI systems must optimise sequences of decisions under uncertainty. It is valuable for adaptive control, interactive systems, and optimisation tasks where long-term outcomes matter more than single-step predictions.
Applications in Machine Learning and Robotics
Common applications include:
- robot navigation and manipulation
- game-playing agents and strategy optimisation
- resource scheduling and operations planning
- recommendation and ranking optimisation
- adaptive tuning of AI-assisted workflows
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Related Resources
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A field of AI where systems learn patterns from data to make predictions or generate content.
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A machine learning method in which models are trained using labelled data.
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