# Per-decision Multi-step Temporal Difference Learning with Control Variates

arXiv: https://arxiv.org/abs/1807.01830 · HTML: https://mlsanity.com/paper/1807.01830/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [Value-aware Importance Weighting for Off-policy Reinforcement Learning](https://mlsanity.com/paper/2306.15625/) (2023)
- [Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning](https://mlsanity.com/paper/2201.05034/) (2022)

## Abstract (arXiv)

Multi-step temporal difference (TD) learning is an important approach in reinforcement learning, as it unifies one-step TD learning with Monte Carlo methods in a way where intermediate algorithms can outperform either extreme. They address a bias-variance trade off between reliance on current estimates, which could be poor, and incorporating longer sampled reward sequences into the updates. Especially in the off-policy setting, where the agent aims to learn about a policy different from the one generating its behaviour, the variance in the updates can cause learning to diverge as the number of sampled rewards used in the estimates increases. In this paper, we introduce per-decision control variates for multi-step TD algorithms, and compare them to existing methods. Our results show that including the control variates can greatly improve performance on both on and off-policy multi-step temporal difference learning tasks.