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complete Jun 9, 2026 updated Jun 9, 2026

PM01 robot competition modeling workflow

Workflow record for digital-twin modeling, rule-engine separation, action prior construction, hierarchical self-play, and BO3 resource scheduling.

#robotics#reinforcement-learning#simulation#resource-scheduling
Domain

Robotics and reinforcement learning

Task

Competition strategy modeling

License

Project-specific report materials

Baseline

Monte Carlo evaluation, risk-adjusted action ranking, and event-driven resource policies

Reproducibility

Repository status

Complete reproduction materials are available but not yet public. A GitHub repository will be linked here when it is ready.

Environment
Language: Python Package: project-specific OS: macOS / Linux Hardware: Simulation-dependent
Reproduction Steps
  1. Separate robot dynamics from competition rule events before policy design.
  2. Construct attack, defense, and meta-skill priors as the high-level action space.
  3. Evaluate attack options with Monte Carlo simulation and CVaR-style risk correction.
  4. Train or analyze single-round tactics through hierarchical self-play and tactical abstraction.
  5. Model timeout, reset, repair, and battery choices as event-driven BO3 resource decisions.
Expected Outputs
  • Action-prior library and risk-adjusted ranking tables.
  • Single-round tactical policy summaries.
  • BO3 resource threshold-policy discussion.

Interpretation Boundary

This is a modeling and strategy report, not a deployed robotics system.

Next steps

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