{
  "id": 12602,
  "url": "https://arxiv.org/abs/2607.19117v1",
  "title": "Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning",
  "summary": "Parameterized action reinforcement learning has shown strong performance in environments requiring both discrete action selection and continuous parameterization. Prior work established the effectiveness of single-agent actor-critic algorithms - Greedy Actor-Critic (GAC), Soft Actor-Critic (SAC), and Truncated Quantile Critics (TQC) - on benchmark parameterized action tasks, but their extension to multi-agent settings remains largely unexplored. This paper presents a comparative study of shared-",
  "authors": "Ubayd Ali Bapoo, Clement N Nyirenda",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T14:03:29.000Z",
  "fetched_at": "2026-07-22T05:10:49.469Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/12602",
  "original_url": "https://arxiv.org/abs/2607.19117v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}