  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 \textit{PettingZoo}-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.
