# Stochastic approximation for efficient LSTD and least squares regression

@inproceedings{PrashanthL2014StochasticAF, title={Stochastic approximation for efficient LSTD and least squares regression}, author={A. PrashanthL. and Nathaniel Korda and R{\'e}mi Munos}, year={2014} }

We propose stochastic approximation based methods with randomization of samples in two different settings - one for policy evaluation using the least squares temporal difference (LSTD) algorithm and the other for solving the least squares problem. We consider a “big data” regime where both the dimension, d, of the data and the number, T, of training samples are large. Through finite time analyses we provide performance bounds for these methods both in high probability and in expectation. In… CONTINUE READING

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 22 REFERENCES

## Transport-Entropy inequalities and deviation estimates for stochastic approximation schemes

VIEW 7 EXCERPTS

HIGHLY INFLUENTIAL

## Least-Squares Policy Iteration

VIEW 6 EXCERPTS

HIGHLY INFLUENTIAL

## Concentration Bounds for Stochastic Approximations

VIEW 4 EXCERPTS

HIGHLY INFLUENTIAL

## Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning

VIEW 5 EXCERPTS

HIGHLY INFLUENTIAL

## A contextual-bandit approach to personalized news article recommendation

VIEW 5 EXCERPTS

HIGHLY INFLUENTIAL

## Approximate Dynamic Programming

VIEW 2 EXCERPTS

HIGHLY INFLUENTIAL

## Finite-sample analysis of least-squares policy iteration

VIEW 2 EXCERPTS