Summary of Intersectionzoo: Eco-driving For Benchmarking Multi-agent Contextual Reinforcement Learning, by Vindula Jayawardana et al.
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
by Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Zhongxia Yan, Cathy Wu
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
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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 Multi-agent reinforcement learning (RL) has seen significant success in simulated and two-player applications, but its real-world applications have been limited due to a lack of generalizability across problem variations. To address this challenge, contextual reinforcement learning (CRL) formalizes learning policies that generalize across problem variations. However, the absence of standardized benchmarks for multi-agent CRL has hindered progress in the field. The proposed IntersectionZoo benchmark suite aims to fill this gap by providing a comprehensive set of benchmarks based on real-world applications, specifically cooperative eco-driving in urban road networks. This task involves controlling a fleet of vehicles to reduce vehicular emissions, capturing real-world problem characteristics such as partial observability and multiple competing objectives. IntersectionZoo is built on data-informed simulations of 16,334 signalized intersections derived from 10 major US cities, modeled using an open-source industry-grade microscopic traffic simulator. The benchmark suite provides one million data-driven traffic scenarios, which are used to evaluate popular multi-agent RL algorithms and human-like driving algorithms. Results show that these popular algorithms struggle to generalize in CRL settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-agent reinforcement learning (RL) is a way for computers to learn from experience and make decisions together. But it’s not as good at solving real-world problems as it is with pretend ones. The problem is that real-world problems are different each time, and the computer has trouble adapting. One solution is called contextual reinforcement learning (CRL), which helps the computer generalize across different situations. However, there isn’t a standard way to test how well CRL algorithms work. To fix this, researchers created a benchmark suite called IntersectionZoo that focuses on cooperative eco-driving in cities. This involves controlling cars to reduce air pollution and taking into account real-world challenges like incomplete information and competing goals. The benchmark uses data from 10 major US cities and provides over one million scenarios for testing algorithms. |
Keywords
* Artificial intelligence * Reinforcement learning
