# PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning

arXiv: https://arxiv.org/abs/2403.17637 · HTML: https://mlsanity.com/paper/2403.17637/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 6 of 6)

- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network](https://mlsanity.com/paper/2009.10601/) (2020)
- [Learning-Based Computation Offloading for IoT Devices with Energy Harvesting](https://mlsanity.com/paper/1712.08768/) (2017)
- [Next Generation Cloud Computing: New Trends and Research Directions](https://mlsanity.com/paper/1707.07452/) (2017)
- [Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm](https://mlsanity.com/paper/1901.10023/) (2019)
- [IFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments](https://mlsanity.com/paper/2109.05636/) (2021)

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

- [FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems](https://mlsanity.com/paper/2506.02668/) (2025)

## Abstract (arXiv)

Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and storage constraints. While traditional optimization falls short in scalability; and heuristic approaches lack in achieving optimal outcomes, Reinforcement Learning (RL) offers a promising avenue by enabling the learning of optimal offloading strategies through iterative interactions. However, the efficacy of RL hinges on access to rich datasets and custom-tailored, realistic training environments. To address this, we introduce PeersimGym, an open-source, customizable simulation environment tailored for developing and optimizing task offloading strategies within computational networks. PeersimGym supports a wide range of network topologies and computational constraints and integrates a textitPettingZoo-based interface for RL agent deployment in both solo and multi-agent setups. Furthermore, we demonstrate the utility of the environment through experiments with Deep Reinforcement Learning agents, showcasing the potential of RL-based approaches to significantly enhance offloading strategies in distributed computing settings. PeersimGym thus bridges the gap between theoretical RL models and their practical applications, paving the way for advancements in efficient task offloading methodologies.