New inference method improves temporal-difference learning accuracy

Researchers have developed a new method called Self-Normalized Inference for Constant-Stepsize Temporal-Difference Learning. This technique allows for more accurate inference from single Markov trajectories by accounting for serial dependence and stepsize-dependent stationary targets. The method provides asymptotically pivotal confidence regions without needing to estimate long-run covariance or select bandwidths, enabling a one-pass implementation with memory that does not grow with trajectory length. Experiments on FrozenLake and Garnet demonstrate its effectiveness in providing coverage for stationary targets and correcting Richardson-Romberg targets. AI

IMPACT Enhances the accuracy and efficiency of learning from sequential data, potentially improving reinforcement learning agents.

RANK_REASON The cluster contains a research paper detailing a new statistical method for temporal-difference learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• Bellman
• Brownian bridge
• Constant-Stepsize Temporal-Difference Learning
• FrozenLake
• garnet group
• Markovian sampling
• Self-Normalized Inference
• Temporal difference learning

• paper
• other

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