GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning
arXiv:2601.20753v4 Announce Type: replace Abstract: Preference-Conditioned Policy Learning (PCPL) in Multi-Objective Reinforcement Learning (MORL) approximates diverse Pareto-optimal solutions by conditioning a single policy on user-specified preferences, enabling run-time adaptation to arbitrary trade-offs without retraining. However, existing PCPL benchmarks are largely restricted to toy tasks and fixed environments, limiting their realism and scalability. To address this gap, we introduce GraphAllocBench, a flexible benchmark built on CityPlannerEnv, a novel graph-based resource allocation
The increasing complexity and practical deployment of multi-objective AI systems necessitate more robust and realistic benchmarking tools to accelerate development.
This new benchmark allows for more flexible and realistic evaluation of AI systems designed for complex resource allocation, directly impacting the development of advanced AI agents.
The ability to condition AI policies on user-specified preferences for multi-objective problems becomes more accessible and practical, moving beyond theoretical toy examples.
- · AI researchers
- · Developers of AI agents
- · Industries with complex resource allocation problems
- · Developers relying on outdated, limited AI benchmarks
Improved development and evaluation of AI agents capable of handling complex, preference-conditioned tasks.
Faster adoption of AI agents in sectors requiring nuanced trade-offs for resource allocation, such as urban planning or logistics.
Enhanced automation and optimization across critical infrastructure, potentially leading to increased efficiency and resilience in smart cities.
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Read at arXiv cs.LG