How does transfer learning work in RL?
mediumAnswer
- Warm-start new policy from related-task policy (fine-tune) or pretrained representation.
- Techniques: (1) Progressive networks (Rusu et al.).
- (2) Distillation from teacher policy.
- (3) Pretrained visual encoders (VC-1, R3M) for vision-based robotics.
- (4) Task embedding + shared trunk.
- Modern: SL-pretrained transformer + RL fine-tune (LLM RLHF is transfer RL).
- Challenge: negative transfer if source task too different.
Check yourself — multiple choice
- No transfer
- Fine-tune / progressive nets / distillation / pretrained visual encoders (VC-1, R3M) / task embedding shared trunk; modern LLM RLHF is transfer RL; watch negative transfer
- Random
- Same as pretrain
Transfer RL: warm-start / distill / pretrained encoders / task embedding.
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