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Cooperative-Competitive Team Play of Real-World Craft Robots

Rui Zhao, Xihui Li, Yizheng Zhang, Yuzhen Liu, Zhong Zhang, Yufeng Zhang, Cheng Zhou, Zhengyou Zhang, Lei Han · Feb 24, 2026 · Citations: 0

General Multi Agent Simulation Env
Open arXiv Find Implementation RSS feed Shortlist (0)

How to use this page

Coverage: Stale

Use this page to decide whether the paper is strong enough to influence an eval design. If the signals below are thin, treat it as background context and compare it against the stronger hub pages before making protocol choices.

Paper metadata checked

Feb 24, 2026, 5:15 PM

Stale

Protocol signals checked

Feb 24, 2026, 5:15 PM

Stale

Signal strength

Low

Model confidence 0.40

Abstract

Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years. However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions. In this work, we first develop a comprehensive robotic system, including simulation, distributed learning framework, and physical robot components. We then propose and evaluate reinforcement learning techniques designed for efficient training of cooperative and competitive policies on this platform. To address the challenges of multi-agent sim-to-real transfer, we introduce Out of Distribution State Initialization (OODSI) to mitigate the impact of the sim-to-real gap. In the experiments, OODSI improves the Sim2Real performance by 20%. We demonstrate the effectiveness of our approach through experiments with a multi-robot car competitive game and a cooperative task in real-world settings.

Use caution before copying this protocol

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Extraction confidence is 0.40 (below strong-reference threshold).
  • No benchmark/dataset or metric anchors were extracted.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

Extraction confidence is 0.40 (below strong-reference threshold).

Trust level

Low

Eval-Fit Score

12/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Reliably Extract

Each protocol field below shows whether the signal looked explicit, partial, or missing in the available metadata. Use this to judge what is safe to trust directly and what still needs full-paper validation.

Human Feedback Types

missing

None explicit

Confidence: Low Source: Persisted extraction missing

No explicit feedback protocol extracted.

Evidence snippet: Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.

Evaluation Modes

partial

Simulation Env

Confidence: Low Source: Persisted extraction evidenced

Includes extracted eval setup.

Evidence snippet: Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.

Quality Controls

missing

Not reported

Confidence: Low Source: Persisted extraction missing

No explicit QC controls found.

Evidence snippet: Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No benchmark anchors detected.

Evidence snippet: Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.

Reported Metrics

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No metric anchors detected.

Evidence snippet: Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.

Rater Population

missing

Unknown

Confidence: Low Source: Persisted extraction missing

Rater source not explicitly reported.

Evidence snippet: Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Unknown
  • Expertise required: General
  • Extraction source: Persisted extraction

Evaluation Lens

  • Evaluation modes: Simulation Env
  • Agentic eval: Multi Agent
  • Quality controls: Not reported
  • Confidence: 0.40
  • Flags: ambiguous

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Deterministic synthesis

Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.

Generated Feb 24, 2026, 5:15 PM · Grounded in abstract + metadata only

Key Takeaways

  • Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.
  • However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions.
  • In this work, we first develop a comprehensive robotic system, including simulation, distributed learning framework, and physical robot components.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Simulation environment) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.
  • However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions.
  • In this work, we first develop a comprehensive robotic system, including simulation, distributed learning framework, and physical robot components.

Why It Matters For Eval

  • Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years.
  • However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Simulation Env

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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