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Rethinking Policy Diversity in Ensemble Policy Gradient in Large-Scale Reinforcement Learning
🤖AI Summary
Researchers propose Coupled Policy Optimization (CPO), a new reinforcement learning method that regulates policy diversity through KL constraints to improve exploration efficiency in large-scale parallel environments. The method outperforms existing baselines like PPO and SAPG across multiple tasks, demonstrating that controlled diverse exploration is key to stable and sample-efficient learning.
Key Takeaways
- →Coupled Policy Optimization uses KL constraints to regulate diversity between policies in ensemble learning methods.
- →The method outperforms strong baselines including SAPG, PBT, and PPO in both sample efficiency and final performance.
- →Excessive exploration can reduce learning quality and training stability, making regulation crucial.
- →Follower policies naturally distribute around leader policies, creating structured exploratory behavior.
- →The research addresses scaling reinforcement learning to tens of thousands of parallel environments.
#reinforcement-learning#policy-optimization#ensemble-methods#machine-learning#exploration#parallel-computing#research#arxiv
Read Original →via arXiv – CS AI
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