2010
NeurIPS
NeurIPS 2010
LSTD with Random Projections
Abstract
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Reinforcement Learning
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Trend Setter
- Stochastic Methods
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Hot Topic Early Bird
- function approximation
Topics
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Optimization & Theory > Theory
Reinforcement Learning > Methods > Deep RL
Reinforcement Learning > Methods > Policy Learning
Reinforcement Learning > Applications > Value Iteration
Machine Learning > Core Methods > Dimensionality Reduction
Machine Learning > Learning Types > Reinforcement Learning
Artificial Intelligence > Core AI > Reinforcement Learning
Deep Learning > Optimization & Theory > Stochastic Methods