# Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning

arXiv: https://arxiv.org/abs/2410.05655 · HTML: https://mlsanity.com/paper/2410.05655/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [Safe and constrained RL](https://mlsanity.com/deeprl/safe-and-constrained-rl/)

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Safe Model-based Reinforcement Learning with Stability Guarantees](https://mlsanity.com/paper/1705.08551/) (2017)
- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [A Lyapunov-based Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/1805.07708/) (2018)
- [DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections](https://mlsanity.com/paper/1906.04733/) (2019)
- [Batch Policy Learning under Constraints](https://mlsanity.com/paper/1903.08738/) (2019)
- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [Safe Reinforcement Learning in Constrained Markov Decision Processes](https://mlsanity.com/paper/2008.06626/) (2020)
- [An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning](https://mlsanity.com/paper/1503.04269/) (2015)
- [A Survey of Constraint Formulations in Safe Reinforcement Learning](https://mlsanity.com/paper/2402.02025/) (2024)
- [Data-Efficient Policy Evaluation Through Behavior Policy Search](https://mlsanity.com/paper/1706.03469/) (2017)
- [The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise](https://mlsanity.com/paper/2401.07844/) (2024)
- [ReVar: Strengthening Policy Evaluation via Reduced Variance Sampling](https://mlsanity.com/paper/2203.04510/) (2022)
- [Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2111.14552/) (2021)
- [Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design](https://mlsanity.com/paper/2301.13734/) (2023)
- [Semi-analytical Industrial Cooling System Model for Reinforcement Learning](https://mlsanity.com/paper/2207.13131/) (2022)
- [Efficient Multi-Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/2408.08706/) (2024)
- [Doubly Optimal Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/2410.02226/) (2024)

## Built on by (most cited first, 6 of 6)

- [Offline Two-Player Zero-Sum Markov Games with KL Regularization](https://mlsanity.com/paper/2605.13025/) (2026)
- [Pessimism-Free Offline Learning in General-Sum Games via KL Regularization](https://mlsanity.com/paper/2605.00264/) (2026)
- [Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation](https://mlsanity.com/paper/2608.24146/) (2026)
- [OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items](https://mlsanity.com/paper/2609.01933/) (2026)
- [Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets](https://mlsanity.com/paper/2609.33289/) (2026)
- [Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage](https://mlsanity.com/paper/2610.04997/) (2026)

## Abstract (arXiv)

In reinforcement learning, classic on-policy evaluation methods often suffer from high variance and require massive online data to attain the desired accuracy. Previous studies attempt to reduce evaluation variance by searching for or designing proper behavior policies to collect data. However, these approaches ignore the safety of such behavior policies -- the designed behavior policies have no safety guarantee and may lead to severe damage during online executions. In this paper, to address the challenge of reducing variance while ensuring safety simultaneously, we propose an optimal variance-minimizing behavior policy under safety constraints. Theoretically, while ensuring safety constraints, our evaluation method is unbiased and has lower variance than on-policy evaluation. Empirically, our method is the only existing method to achieve both substantial variance reduction and safety constraint satisfaction. Furthermore, we show our method is even superior to previous methods in both variance reduction and execution safety.