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CALF: Communication-Aware Learning Framework for Distributed Reinforcement Learning

arXiv:2603.12543v1 Announce Type: new 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' · · 1 min read · 12 views
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arXiv:2603.12543v1 Announce Type: new 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.

Executive Summary

The CALF framework addresses a critical issue in distributed reinforcement learning, where standard training methods assume zero-latency interaction, leading to severe performance degradation under realistic network conditions. By training policies under realistic network models during simulation, CALF substantially reduces deployment performance gaps compared to network-agnostic baselines. This breakthrough has significant implications for the deployment of distributed policies across heterogeneous hardware, where communication constraints can be explicitly modelled during training to enable robust real-world execution. The findings of this study highlight the importance of network conditions as a major axis of sim-to-real transfer for Wi-Fi-like distributed deployments, complementing existing approaches such as physics and visual domain randomisation.

Key Points

  • ▸ CALF framework trains policies under realistic network models during simulation
  • ▸ Substantial reduction in deployment performance gaps compared to network-agnostic baselines
  • ▸ Explicit modelling of communication constraints during training enables robust real-world execution

Merits

Strength

The CALF framework's ability to address the critical issue of network latency in distributed reinforcement learning is a significant strength, highlighting its potential to improve the performance of deployed policies.

Novelty

The introduction of a communication-aware learning framework is a novel approach that differentiates CALF from existing methods, offering a more robust and reliable alternative for distributed policy deployment.

Experimental validity

The systematic experiments conducted in the study demonstrate the efficacy of the CALF framework, providing strong evidence for its benefits in reducing deployment performance gaps.

Demerits

Limitation

The study's focus on Wi-Fi-like distributed deployments may limit the generalizability of the findings to other types of networks or deployment scenarios.

Scalability

The scalability of the CALF framework in large-scale distributed systems is not fully explored in the study, representing a potential area for future research.

Complexity

The explicit modelling of communication constraints during training may introduce additional complexity to the learning process, potentially requiring more computational resources or expertise.

Expert Commentary

The CALF framework represents a significant breakthrough in the field of distributed reinforcement learning, addressing a critical issue that has hindered the deployment of robust and reliable policies. By training policies under realistic network models, CALF offers a more nuanced understanding of the complex interactions between agents and environments. However, as with any novel approach, there are potential limitations and challenges to consider, such as scalability and complexity. Further research is needed to fully explore the potential of CALF and its applications in diverse fields. Nonetheless, the study's findings and the CALF framework itself have the potential to significantly impact the development of distributed policies and the broader field of reinforcement learning.

Recommendations

  • ✓ Further research is needed to explore the scalability and complexity of the CALF framework in large-scale distributed systems.
  • ✓ The CALF framework should be tested and validated in a variety of deployment scenarios, including different types of networks and hardware configurations.

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