Researchers fix synthetic data failures in reinforcement learning policy optimization

Researchers have identified and addressed algorithmic failures in Model-Based Policy Optimization (MBPO), a technique used in reinforcement learning. The study found that MBPO can underperform compared to other methods like Soft Actor-Critic (SAC) due to scale mismatches and residual next-state prediction, which lead to critic underestimation and unreliable synthetic data. A new method called Fixing That Free Lunch (FTFL) was introduced, which combines target normalization and direct next-state prediction to resolve these issues, showing improved performance on several benchmark tasks. AI

IMPACT Identifies and solves specific failure modes in model-based RL, potentially improving the reliability of synthetic data generation for training.

RANK_REASON Academic paper detailing algorithmic failures and proposing a solution in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Brett Barkley
• DeepMind Control Suite
• Fixing That Free Lunch
• Model-Based Policy Optimization
• MuJoCo
• OpenAI Gym
• Soft Actor-Critic

• paper
• other

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