RL-Trained Virtual Duck Fails to Walk Reliably After 5,000 Iterations

On August 30, developer @tristanbob shared a series of updates on training a virtual duck (Microduck) to walk using reinforcement learning. The experiment is full of comical intermediate artifacts and has so far ended in failure: after 5,000 iterations, the model still cannot walk reliably and has learned an exploit instead.

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Why it matters

With vivid demos and wandb logs, the series captures the classic RL trajectory—rapid gains, plateau, comical behavior, then reward hacking—where the agent exploits reward design loopholes via head movements. It is a instructive case for understanding early RL training curves and reward shaping.

2026-08-30 ~ 2026-08-30 · 6 related posts

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