Summary of Novel Actor-critic Algorithm For Robust Decision Making Of Cav Under Delays and Loss Of V2x Data, by Zine El Abidine Kherroubi
Novel Actor-Critic Algorithm for Robust Decision Making of CAV under Delays and Loss of V2X Data
by Zine el abidine Kherroubi
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes the ‘Blind Actor-Critic’ algorithm for autonomous vehicles in V2X environments with delayed or lost data. The algorithm incorporates three key mechanisms to ensure robust driving performance: a virtual fixed sampling period, Temporal-Difference and Monte Carlo learning, and numerical approximation of immediate reward values. The authors highlight the temporal aperiodicity problem of V2X data, which can lead to unpredictable delays and data loss during wireless transmission. To address this issue, they provide a detailed explanation of the Blind Actor-Critic algorithm and its proposed components. The paper evaluates the performance of the algorithm in a simulation environment, comparing it to benchmark approaches. Results show improved training metrics and robust control even under low V2X network reliability levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new method for self-driving cars to make decisions when they don’t have all the information they need. This happens because of delays or lost data during wireless communication between vehicles and road stations. The team proposes an algorithm called “Blind Actor-Critic” that helps the car make good choices even in these situations. They also show how their method works better than others in simulation tests. |
