In the context of the multi-armed bandit problem, what is regret?
- The feeling of loss and remorse
- An optimization metric
- A random variable
- An arm selection policy
In the context of the multi-armed bandit problem, regret is an optimization metric that quantifies how much an agent's total reward falls short of the best possible reward it could have achieved by always choosing the best arm. It's a way to measure how well an agent's arm selection policy performs.
Loading...
Related Quiz
- In which learning approach does the model learn to make decisions by receiving rewards or penalties for its actions?
- In reinforcement learning scenarios where rapid feedback is not available, which strategy, exploration or exploitation, could be potentially riskier?
- Why is balancing exploration and exploitation crucial in reinforcement learning?
- In SVM, what does the term "kernel" refer to?
- Why is feature selection important in building machine learning models?