Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking
📰 ArXiv cs.AI
Learn how to apply Multi-Agent Reinforcement Learning (MARL) to Vehicle-to-Everything (V2X) resource allocation, overcoming challenges through benchmarking
Action Steps
- Implement MARL algorithms for V2X resource allocation using Python and libraries like TensorFlow or PyTorch
- Evaluate the performance of different MARL approaches using benchmarking tools
- Address non-stationarity and coordination difficulties by applying techniques like centralized critics or mean-field reinforcement learning
- Mitigate partial observability by using techniques like attention mechanisms or graph neural networks
- Analyze the impact of large action spaces on MARL performance and apply techniques like action pruning or discretization
Who Needs to Know This
Researchers and engineers working on V2X networks and MARL can benefit from this article to improve resource allocation efficiency and robustness
Key Insight
💡 Benchmarking is crucial to disentangle MARL challenges and improve the efficiency and robustness of V2X resource allocation
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Apply MARL to V2X resource allocation and overcome key challenges through benchmarking! #MARL #V2X #ResourceAllocation
Key Takeaways
Learn how to apply Multi-Agent Reinforcement Learning (MARL) to Vehicle-to-Everything (V2X) resource allocation, overcoming challenges through benchmarking
Full Article
Title: Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking
Abstract:
arXiv:2603.06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications. Multi-agent reinforcement learning (MARL) has emerged as a promising approach for this problem. However, key MARL challenges, including non-stationarity, coordination difficulty, large action space, partial observability, and limited robus
Abstract:
arXiv:2603.06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications. Multi-agent reinforcement learning (MARL) has emerged as a promising approach for this problem. However, key MARL challenges, including non-stationarity, coordination difficulty, large action space, partial observability, and limited robus
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