# A Concentration Bound for LSPE(λ)

arXiv: https://arxiv.org/abs/2111.02644 · HTML: https://mlsanity.com/paper/2111.02644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Is Q-learning Provably Efficient?](https://mlsanity.com/paper/1807.03765/) (2018)
- [A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation](https://mlsanity.com/paper/1806.02450/) (2018)
- [Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning](https://mlsanity.com/paper/1902.00923/) (2019)
- [Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning](https://mlsanity.com/paper/2002.00260/) (2020)
- [Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction](https://mlsanity.com/paper/2006.03041/) (2020)
- [Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis](https://mlsanity.com/paper/2102.06548/) (2021)
- [Stochastic approximation with cone-contractive operators: Sharp ℓ_∞-bounds for Q-learning](https://mlsanity.com/paper/1905.06265/) (2019)
- [Variance-reduced Q-learning is minimax optimal](https://mlsanity.com/paper/1906.04697/) (2019)
- [A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants](https://mlsanity.com/paper/2102.01567/) (2021)
- [Concentration of Contractive Stochastic Approximation and Reinforcement Learning](https://mlsanity.com/paper/2106.14308/) (2021)
- [A Concentration Bound for Stochastic Approximation via Alekseev's Formula](https://mlsanity.com/paper/1506.08657/) (2015)
- [Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling](https://mlsanity.com/paper/1306.2557/) (2013)
- [A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation](https://mlsanity.com/paper/1909.04299/) (2019)

## Abstract (arXiv)

The popular LSPE(λ) algorithm for policy evaluation is revisited to derive a concentration bound that gives high probability performance guarantees from some time on.