# Reinforcement Learning via Conservative Agent for Environments with Random Delays

arXiv: https://arxiv.org/abs/2507.18992 · HTML: https://mlsanity.com/paper/2507.18992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Humanoid Parkour Learning](https://mlsanity.com/paper/2406.10759/) (2024)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Reinforcement Learning with Random Delays](https://mlsanity.com/paper/2010.02966/) (2020)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [Delay-Aware Model-Based Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/2005.05440/) (2020)
- [Setting up a Reinforcement Learning Task with a Real-World Robot](https://mlsanity.com/paper/1803.07067/) (2018)
- [Acting in Delayed Environments with Non-Stationary Markov Policies](https://mlsanity.com/paper/2101.11992/) (2021)
- [At Human Speed: Deep Reinforcement Learning with Action Delay](https://mlsanity.com/paper/1810.07286/) (2018)
- [SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II](https://mlsanity.com/paper/2012.13169/) (2020)
- [Delayed Reinforcement Learning by Imitation](https://mlsanity.com/paper/2205.05569/) (2022)
- [Intrinsically motivated reinforcement learning for human-robot interaction in the real-world](https://mlsanity.com/paper/1804.05259/) (2018)
- [Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays](https://mlsanity.com/paper/2402.03141/) (2024)
- [Variational Delayed Policy Optimization](https://mlsanity.com/paper/2405.14226/) (2024)
- [Delays in Reinforcement Learning](https://mlsanity.com/paper/2309.11096/) (2023)

## Built on by (most cited first, 1 of 1)

- [Delayed homomorphic reinforcement learning for environments with delayed feedback](https://mlsanity.com/paper/2604.03641/) (2026)

## Abstract (arXiv)

Real-world reinforcement learning applications are often hindered by delayed feedback from environments, which violates the Markov assumption and introduces significant challenges. Although numerous delay-compensating methods have been proposed for environments with constant delays, environments with random delays remain largely unexplored due to their inherent variability and unpredictability. In this study, we propose a simple yet robust agent for decision-making under random delays, termed the conservative agent, which reformulates the random-delay environment into its constant-delay equivalent. This transformation enables any state-of-the-art constant-delay method to be directly extended to the random-delay environments without modifying the algorithmic structure or sacrificing performance. We evaluate the conservative agent-based algorithm on continuous control tasks, and empirical results demonstrate that it significantly outperforms existing baseline algorithms in terms of asymptotic performance and sample efficiency.