Model-based vs model-free RL — when do you prefer each?
mediumAnswer
- Model-based RL learns a model of the environment (transitions and rewards) and uses it to plan (e.g., MuZero, Dreamer).
- It is more sample-efficient — great when real interactions are expensive (robotics, healthcare).
- Model-free RL learns policy or values directly from experience — simpler, and it wins when the environment is complex/hard to model.
- Modern successes often combine both.
Check yourself — multiple choice
- Model-based RL never plans ahead
- Model-based RL is generally more sample-efficient
- Model-free RL learns transition models
- MuZero is a model-free algorithm
Learning a model gives you a simulator you can plan in — much better sample efficiency.
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