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arXiv Machine Learning By Yuda Song, Dhruv Rohatgi, Aarti Singh, J. Andrew Bagnell

To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper examines the trade-off between using privileged expert distillation and standard reinforcement learning in partially observable settings. By analyzing a theoretical model called the perturbed Block MDP and running experiments on simulated locomotion tasks, it finds that the effectiveness of distillation depends on the stochasticity of latent dynamics and that the optimal latent policy is not always the best to distill. These insights provide guidelines for better leveraging privileged information to improve policy learning efficiency.

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