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Why does my reinforcement learning agent behave unpredictably in real environments?
This happens because simulations never perfectly match reality. The model learns simulation-specific dynamics that do not transfer. This is known as the sim-to-real gap. Even tiny differences in friction, timing, or noise can break learned policies. Domain randomization and real-world fine-tuning heRead more
This happens because simulations never perfectly match reality. The model learns simulation-specific dynamics that do not transfer.
This is known as the sim-to-real gap. Even tiny differences in friction, timing, or noise can break learned policies.
Domain randomization and real-world fine-tuning help close this gap.
Common mistakes:
Overfitting to simulation
No noise injection
No real-world adaptation
The practical takeaway is that real environments require real data.
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