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LSTD with Random Projections
Mohammad Ghavamzadeh · Alessandro Lazaric · Odalric-Ambrym Maillard · Remi Munos

Tue Dec 07 11:25 AM -- 11:30 AM (PST) @ Regency Ballroom

We consider the problem of reinforcement learning in high-dimensional spaces when the number of features is bigger than the number of samples. In particular, we study the least-squares temporal difference (LSTD) learning algorithm when a space of low dimension is generated with a random projection from a high-dimensional space. We provide a thorough theoretical analysis of the LSTD with random projections and derive performance bounds for the resulting algorithm. We also show how the error of LSTD with random projections is propagated through the iterations of a policy iteration algorithm and provide a performance bound for the resulting least-squares policy iteration (LSPI) algorithm.

Author Information

Mohammad Ghavamzadeh (Facebook AI Research)
Alessandro Lazaric (Facebook Artificial Intelligence Research)
Odalric-Ambrym Maillard (INRIA)
Remi Munos (Google DeepMind)

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