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Pessimistic Auxiliary Policy for Offline Reinforcement Learning
🤖AI Summary
Researchers developed a new pessimistic auxiliary policy for offline reinforcement learning that reduces error accumulation by sampling more reliable actions. The approach maximizes the lower confidence bound of Q-functions to avoid high-value actions with potentially high errors during training.
Key Takeaways
- →New pessimistic auxiliary policy addresses error accumulation issues in offline reinforcement learning systems.
- →The method maximizes lower confidence bounds of Q-functions to sample more reliable actions during training.
- →Approach reduces approximation errors by avoiding high-value actions with high uncertainty.
- →Extensive experiments show the strategy improves efficacy of other offline RL approaches.
- →Solution enables safer learning from pre-collected datasets without real-time interaction risks.
#reinforcement-learning#offline-rl#machine-learning#ai-research#pessimistic-policy#q-learning#error-reduction
Read Original →via arXiv – CS AI
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