Consider a robot that learns to navigate a maze. Instead of learning the value of each state or action, it tries to optimize its actions based on direct feedback. This approach is most similar to which reinforcement learning method?
- Monte Carlo Methods
- Temporal Difference Learning (TD)
- Actor-Critic Method
- Q-Learning
In this context, the robot is optimizing actions based on direct feedback, which is a characteristic of the Actor-Critic method. This method combines value-based and policy-based approaches, making it similar to the situation described.
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