A Concentration Bound for LSPE(λ)
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 33 references that are in the map. First posted on arXiv 4 Nov 2021.
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13 of 33 references.
References · 13
- 2018Is Q-learning Provably Efficient?cited by 485
- 2018A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximationcited by 225
- 2019Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learningcited by 165
- 2020Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learningcited by 93
- 2020Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reductioncited by 93
- 2021Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysiscited by 89
- 2019Stochastic approximation with cone-contractive operators: Sharp ℓ_∞-bounds for Q-learningcited by 74
- 2019Variance-reduced Q-learning is minimax optimalcited by 72
- 2021A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variantscited by 57
- 2021Concentration of Contractive Stochastic Approximation and Reinforcement Learningcited by 23
- 2015A Concentration Bound for Stochastic Approximation via Alekseev's Formulacited by 16
- 2013Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform samplingcited by 13
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