Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts

• URL: http://arxiv.org/abs/2407.18074v2
• Date: Mon, 7 Oct 2024 16:46:42 GMT
• Title: Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts
• Authors: Dima Ivanov, Paul Dütting, Inbal Talgam-Cohen, Tonghan Wang, David C. Parkes,
• Abstract summary: We propose a framework where a principal guides an agent in a Markov Decision Process (MDP) using a series of contracts. We present and analyze a meta-algorithm that iteratively optimize the policies of the principal and agent. We then scale our algorithm with deep Q-learning and analyze its convergence in the presence of approximation error.
• Score: 20.8288955218712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing deployment of AI is shaping the future landscape of the internet, which is set to become an integrated ecosystem of AI agents. Orchestrating the interaction among AI agents necessitates decentralized, self-sustaining mechanisms that harmonize the tension between individual interests and social welfare. In this paper we tackle this challenge by synergizing reinforcement learning with principal-agent theory from economics. Taken separately, the former allows unrealistic freedom of intervention, while the latter struggles to scale in sequential settings. Combining them achieves the best of both worlds. We propose a framework where a principal guides an agent in a Markov Decision Process (MDP) using a series of contracts, which specify payments by the principal based on observable outcomes of the agent's actions. We present and analyze a meta-algorithm that iteratively optimizes the policies of the principal and agent, showing its equivalence to a contraction operator on the principal's Q-function, and its convergence to subgame-perfect equilibrium. We then scale our algorithm with deep Q-learning and analyze its convergence in the presence of approximation error, both theoretically and through experiments with randomly generated binary game-trees. Extending our framework to multiple agents, we apply our methodology to the combinatorial Coin Game. Addressing this multi-agent sequential social dilemma is a promising first step toward scaling our approach to more complex, real-world instances.

Related papers

• Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm [85.7583231789615]
  6G positions intelligence as a native network capability, transforming the design of radio access networks (RANs)<n>Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles.<n>Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration.
  arXiv  Detail & Related papers  (2025-12-04T03:09:33Z)
• Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning [57.23345786304694]
  We introduce a framework for prospective learning and embedded agency centered on self-prediction.<n>We show that in multi-agent settings, self-prediction enables agents to reason about others running similar algorithms.<n>We extend the theory of AIXI, and study universally intelligent embedded agents which start from a Solomonoff prior.
  arXiv  Detail & Related papers  (2025-11-27T08:46:48Z)
• Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning [84.70211451226835]
  Large Language Model (LLM) Agents are constrained by a dependency on human-curated data.<n>We introduce Agent0, a fully autonomous framework that evolves high-performing agents without external data.<n>Agent0 substantially boosts reasoning capabilities, improving the Qwen3-8B-Base model by 18% on mathematical reasoning and 24% on general reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-11-20T05:01:57Z)
• Maestro: Learning to Collaborate via Conditional Listwise Policy Optimization for Multi-Agent LLMs [23.590034731179824]
  We present Through Role Orchestration (Maestro), a principled paradigm for collaboration that structurally decouples cognitive modes.<n>Maestro uses a collective of parallel Execution Agents for diverse exploration and a specialized Central Agent for convergent, evaluative synthesis.<n>Experiments on mathematical reasoning and general problem-solving benchmarks demonstrate that Maestro, coupled with CLPO, consistently outperforms existing state-of-the-art multi-agent approaches.
  arXiv  Detail & Related papers  (2025-11-08T21:01:27Z)
• The Collaboration Paradox: Why Generative AI Requires Both Strategic Intelligence and Operational Stability in Supply Chain Management [0.0]
  The rise of autonomous, AI-driven agents in economic settings raises critical questions about their emergent strategic behavior.<n>This paper investigates these dynamics in the cooperative context of a multi-echelon supply chain.<n>Our central finding is the "collaboration paradox": a novel, catastrophic failure mode where theoretically superior collaborative AI agents perform even worse than non-AI baselines.
  arXiv  Detail & Related papers  (2025-08-19T15:31:23Z)
• Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
  The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
  arXiv  Detail & Related papers  (2025-07-28T17:58:12Z)
• Scalable, Symbiotic, AI and Non-AI Agent Based Parallel Discrete Event Simulations [0.0]
  This paper introduces a novel parallel discrete event simulation (PDES) based methodology to combine multiple AI and non-AI agents.<n>We evaluate our approach by solving four problems from four different domains and comparing the results with those from AI models alone.<n>Results show that overall accuracy of our approach is 68% where as the accuracy of vanilla models is less than 23%.
  arXiv  Detail & Related papers  (2025-05-28T17:50:01Z)
• AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges [0.36868085124383626]
  This study critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis.<n>Generative AI is positioned as a precursor, with AI Agents advancing through tool integration, prompt engineering, and reasoning enhancements.<n>Agentic AI systems represent a paradigmatic shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and orchestrated autonomy.
  arXiv  Detail & Related papers  (2025-05-15T16:21:33Z)
• Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach [35.05793485239977]
  We propose AgentNet, a novel framework for supporting interaction, collaborative learning, and knowledge transfer among AI agents. We consider two application scenarios, digital-twin-based industrial automation and metaverse-based infotainment system, to describe how to apply AgentNet.
  arXiv  Detail & Related papers  (2025-03-20T00:48:44Z)
• From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning [62.54484062185869]
  We introduce StepAgent, which utilizes step-wise reward to optimize the agent's reinforcement learning process. We propose implicit-reward and inverse reinforcement learning techniques to facilitate agent reflection and policy adjustment.
  arXiv  Detail & Related papers  (2024-11-06T10:35:11Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring [105.13668993076801]
  A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees. We study this question in a general framework for interactive decision making with multiple agents. We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
  arXiv  Detail & Related papers  (2023-05-01T06:46:22Z)
• Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning [17.101534531286298]
  We construct a Nash-level policy model based on a conditional hypernetwork shared by all agents. This approach allows for asymmetric training with symmetric execution, with each agent responding optimally conditioned on the decisions made by superior agents. Experiments demonstrate that our method effectively converges to the SE policies in repeated matrix game scenarios.
  arXiv  Detail & Related papers  (2023-04-20T14:47:54Z)
• Artificial Intelligence and Dual Contract [2.1756081703276]
  We develop a model where two principals, each equipped with independent Q-learning algorithms, interact with a single agent. Our findings reveal that the strategic behavior of AI principals hinges crucially on the alignment of their profits.
  arXiv  Detail & Related papers  (2023-03-22T07:31:44Z)
• Uncoupled Learning of Differential Stackelberg Equilibria with Commitments [43.098826226730246]
  We present uncoupled'' learning dynamics based on zeroth-order gradient estimators. We prove that they converge to differential Stackelberg equilibria under the same conditions as previous coupled methods. We also present an online mechanism by which symmetric learners can negotiate leader-follower roles.
  arXiv  Detail & Related papers  (2023-02-07T12:46:54Z)
• Decentralized Cooperative Multi-Agent Reinforcement Learning with Exploration [35.75029940279768]
  We study multi-agent reinforcement learning in the most basic cooperative setting -- Markov teams. We propose an algorithm in which each agent independently runs a stage-based V-learning style algorithm. We show that the agents can learn an $epsilon$-approximate Nash equilibrium policy in at most $proptowidetildeO (1/epsilon4)$ episodes.
  arXiv  Detail & Related papers  (2021-10-12T02:45:12Z)
• Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values [68.8204255655161]
  This study proposes a novel approach to explain cooperative strategies in multiagent RL using Shapley values. Results could have implications for non-discriminatory decision making, ethical and responsible AI-derived decisions or policy making under fairness constraints.
  arXiv  Detail & Related papers  (2021-10-04T10:28:57Z)
• Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous [66.6895109554163]
  Underlying the human ability to align goals with other agents is their ability to predict the intentions of others and actively update their own plans. We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous.
  arXiv  Detail & Related papers  (2020-03-15T19:49:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.