CALF: Communication-Aware Learning Framework for Distributed Reinforcement Learning

Carlos Purves; Pietro Lio'

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arXiv · 2603.12543

CALF: Communication-Aware Learning Framework for Distributed Reinforcement Learning

Carlos Purves·Pietro Lio'

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Abstract

Distributed reinforcement learning policies face network delays, jitter, and packet loss when deployed across edge devices and cloud servers. Standard RL training assumes zero-latency interaction, causing severe performance degradation under realistic network conditions. We introduce CALF (Communication-Aware Learning Framework), which trains policies under realistic network models during simulation. Systematic experiments demonstrate that network-aware training substantially reduces deployment performance gaps compared to network-agnostic baselines. Distributed policy deployments across heterogeneous hardware validate that explicitly modelling communication constraints during training enables robust real-world execution. These findings establish network conditions as a major axis of sim-to-real transfer for Wi-Fi-like distributed deployments, complementing physics and visual domain randomisation.

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Carlos Purves, Pietro Lio'. 2026-03-13. CALF: Communication-Aware Learning Framework for Distributed Reinforcement Learning. https://arxiv.org/abs/2603.12543

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