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Representation over Routing: Overcoming Surrogate Hacking in Multi-Timescale PPO

TL;DR AI

Key summary

2 min read
  1. Researchers propose Target Decoupling for multi-timescale actor-critic methods to improve training stability.

  2. The approach removes routing aggregation from the actor and moves multi-horizon fitting to the critic.

  3. It addresses two failure modes, including surrogate objective hacking and policy collapse.

  4. On LunarLander-v2, the decoupled PPO design shows more reliable performance on delayed-reward tasks.

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