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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
When Do Options Help? Policy Necrosis and Redundant Coverage in Option-Critic
TL;DR
Option-Critic framework learns sub-policies called options, but the termination rule can hinder exploration, leading to policy necrosis. Adding more options reduces the likelihood of failure in the same state, improving overall performance.
✦ Why It Matters
Evaluate your reinforcement learning models for policy necrosis and enhance exploration strategies today.
Key Takeaways
- Incorporate exploration strategies in option-critic frameworks.
- Monitor for policy necrosis in reinforcement learning models.
- Consider the impact of termination rules on learning efficiency.
- Use multiple options to reduce failure rates in specific states.
How It Works
Option-critic learns options by maximizing returns, but if the termination rule and option selection policy read the same values, it can lead to immediate termination, blocking exploration. This results in policy necrosis, where the model fails to update its actions based on new information.