Research questions Q-function pre-training effectiveness in reinforcement learning

A new research paper explores the effectiveness of pre-training Q-functions in reinforcement learning (RL). The study found that pre-training Q-functions often provides minimal benefit compared to random initialization when fine-tuning a policy. This is attributed to a mismatch between the Q-function learned during pre-training and the one that online fine-tuning converges to. To address this, the researchers propose Initialization via Policy Ensemble (IPE), a method that uses diverse policies to bootstrap Q-function learning, showing an average 1.26x improvement in fine-tuning performance on continuous control benchmarks. AI

IMPACT Challenges conventional wisdom in RL pre-training, potentially shifting best practices for policy optimization.

RANK_REASON Academic paper published on arXiv detailing novel research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Initialization via Policy Ensemble
• IPE
• Q-function
• reinforcement learning

• paper
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