Double Oracle Algorithm for Game-Theoretic Robot Allocation on Graphs
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Abstract (arXiv)
We study the problem of game-theoretic robot allocation where two players strategically allocate robots to compete for multiple sites of interest. Robots possess offensive or defensive capabilities to interfere and weaken their opponents to take over a competing site. This problem belongs to the conventional Colonel Blotto Game. Considering the robots' heterogeneous capabilities and environmental factors, we generalize the conventional Blotto game by incorporating heterogeneous robot types and graph constraints that capture the robot transitions between sites. Then we employ the Double Oracle Algorithm (DOA) to solve for the Nash equilibrium of the generalized Blotto game. Particularly, for cyclic-dominance-heterogeneous (CDH) robots that inhibit each other, we define a new transformation rule between any two robot types. Building on the transformation, we design a novel utility function to measure the game's outcome quantitatively. Moreover, we rigorously prove the correctness of the designed utility function. Finally, we conduct extensive simulations to demonstrate the effectiveness of DOA on computing Nash equilibrium for homogeneous, linear heterogeneous, and CDH robot allocation on graphs.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Dec 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024Policy Space Response Oracles: A Surveycited by 10
- 2026GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learningcited by 2
- 2025Reinforcement Learning for Game-Theoretic Resource Allocation on Graphscited by 1
- 2025HGFormer: A Hierarchical Graph Transformer Framework for Two-Stage Colonel Blotto Games via Reinforcement Learningcited by 0
- 2026GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning28 Jan 2026
- 2025HGFormer: A Hierarchical Graph Transformer Framework for Two-Stage Colonel Blotto Games via Reinforcement Learning10 Jun 2025
- 2025Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs8 May 2025
- 2024Policy Space Response Oracles: A Survey4 Mar 2024