Statistical Analysis
Machine Learning
The fundamental units in reinforcement learning that combine a particular situation with a possible action that can be taken.
Detailed Explanation
State-Action Pairs in reinforcement learning represent the combination of a specific environment state and a corresponding action an agent can take. They are fundamental for decision-making, as they help the model evaluate potential outcomes. By analyzing state-action pairs, the agent learns optimal policies to maximize rewards over time. They form the basis for algorithms like Q-learning and policy iteration.
Use Cases
- •Optimizing a robot’s navigation by evaluating state-action pairs to determine safest, most efficient routes in dynamic environments.
Related Terms
Other terms in the Machine Learning category