When training a robot to play a game where it gets points for good moves and loses points for bad ones, which learning approach would be most appropriate?
- Reinforcement learning
- Semi-supervised learning
- Supervised learning
- Unsupervised learning
Reinforcement learning is the most appropriate approach for training a robot to play a game where it receives rewards for good moves and penalties for bad ones. In reinforcement learning, the agent learns through trial and error, optimizing its actions to maximize cumulative rewards. Supervised learning would require explicit labels for each move, which are typically not available in this context. Unsupervised and semi-supervised learning are not suitable for tasks with rewards and penalties.
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