Random Network Distillation (RND) — mechanism.
hardAnswer
- Fixed random target network initialized randomly + trainable predictor .
- Intrinsic reward = ||||².
- Predictor learns quickly on frequently-visited states → low intrinsic reward.
- Rare states have high error → high intrinsic reward.
- Simple, cheap, effective — solved Montezuma's Revenge (Burda et al. 2018).
- Foundation of modern exploration methods.
Check yourself — multiple choice
- Same as count
- Fixed random target NN + trainable predictor; intrinsic reward = ||pred - target||²; predictor learns fast on visited states → high error = novel state; solved Montezuma
- Random
- Not real
RND: predict-random-network error as novelty; solved Montezuma.
#exploration#deep-rl
Practise Reinforcement Learning
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