AgentMixer: Multi-Agent Correlated Policy Factorization

• URL: http://arxiv.org/abs/2401.08728v3
• Date: Fri, 13 Dec 2024 11:12:33 GMT
• Title: AgentMixer: Multi-Agent Correlated Policy Factorization
• Authors: Zhiyuan Li, Wenshuai Zhao, Lijun Wu, Joni Pajarinen,
• Abstract summary: AgentMixer combines individual partially observable policies into a joint fully observable policy non-linearly.<n>AgentMixer outperforms or matches state-of-the-art methods in the Multi-Agent MuJoCo, SMAC-v2, Matrix Game, and Predator-Prey benchmarks.
• Score: 35.73799473896975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-agent reinforcement learning, centralized training with decentralized execution (CTDE) methods typically assume that agents make decisions based on their local observations independently, which may not lead to a correlated joint policy with coordination. Coordination can be explicitly encouraged during training and individual policies can be trained to imitate the correlated joint policy. However, this may lead to an \textit{asymmetric learning failure} due to the observation mismatch between the joint and individual policies. Inspired by the concept of correlated equilibrium, we introduce a \textit{strategy modification} called AgentMixer that allows agents to correlate their policies. AgentMixer combines individual partially observable policies into a joint fully observable policy non-linearly. To enable decentralized execution, we introduce \textit{Individual-Global-Consistency} to guarantee mode consistency during joint training of the centralized and decentralized policies and prove that AgentMixer converges to an $\epsilon$-approximate Correlated Equilibrium. In the Multi-Agent MuJoCo, SMAC-v2, Matrix Game, and Predator-Prey benchmarks, AgentMixer outperforms or matches state-of-the-art methods.

Related papers

• Centralized Adaptive Sampling for Reliable Co-Training of Independent Multi-Agent Policies [3.5253513747455303]
  Independent on-policy policy gradient algorithms are widely used for multi-agent reinforcement learning (MARL) in cooperative and no-conflict games.<n>They are known to converge suboptimally when each agent's policy gradient points toward a suboptimal equilibrium.<n>We introduce an adaptive action sampling approach to reduce joint sampling error.
  arXiv  Detail & Related papers  (2025-08-01T20:07:25Z)
• CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-Making [2.4555276449137042]
  Robust coordination skills enable agents to operate cohesively in shared environments, together towards a common goal and, ideally, individually without hindering each other's progress. This paper presents Coordinated QMIX, a novel training framework for decentralized agents that enables emergent coordination through flexible policies, allowing at the same time independent decision-making at individual level.
  arXiv  Detail & Related papers  (2023-08-21T13:45:44Z)
• Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning [7.784991832712813]
  We introduce a Bayesian network to inaugurate correlations between agents' action selections in their joint policy. We develop practical algorithms to learn the context-aware Bayesian network policies. Empirical results on a range of MARL benchmarks show the benefits of our approach.
  arXiv  Detail & Related papers  (2023-06-02T21:22:27Z)
• 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)
• Decentralized Policy Optimization [21.59254848913971]
  We propose textitdecentralized policy optimization (DPO), a decentralized actor-critic algorithm with monotonic improvement and convergence guarantee. Empirically, we compare DPO with IPPO in a variety of cooperative multi-agent tasks, covering discrete and continuous action spaces, and fully and partially observable environments.
  arXiv  Detail & Related papers  (2022-11-06T05:38:23Z)
• More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization [21.10461189367695]
  In cooperative multi-agent reinforcement learning (MARL), combining value decomposition with actor-critic enables agents learn policies. Agents are commonly assumed to be independent of each other, even in centralized training. We propose multi-agent conditional policy factorization (MACPF) which takes more centralized training but still enables decentralized execution.
  arXiv  Detail & Related papers  (2022-09-26T13:29:22Z)
• Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO [66.5384483339413]
  We present a new monotonic improvement guarantee for optimizing decentralized policies in cooperative Multi-Agent Reinforcement Learning (MARL) We show that a trust region constraint can be effectively enforced in a principled way by bounding independent ratios based on the number of agents in training.
  arXiv  Detail & Related papers  (2022-01-31T20:39:48Z)
• Multi-Agent MDP Homomorphic Networks [100.74260120972863]
  In cooperative multi-agent systems, complex symmetries arise between different configurations of the agents and their local observations. Existing work on symmetries in single agent reinforcement learning can only be generalized to the fully centralized setting. This paper introduces Multi-Agent MDP Homomorphic Networks, a class of networks that allows distributed execution using only local information.
  arXiv  Detail & Related papers  (2021-10-09T07:46:25Z)
• Dealing with Non-Stationarity in Multi-Agent Reinforcement Learning via Trust Region Decomposition [52.06086375833474]
  Non-stationarity is one thorny issue in multi-agent reinforcement learning. We introduce a $delta$-stationarity measurement to explicitly model the stationarity of a policy sequence. We propose a trust region decomposition network based on message passing to estimate the joint policy divergence.
  arXiv  Detail & Related papers  (2021-02-21T14:46:50Z)
• Implicit Distributional Reinforcement Learning [61.166030238490634]
  implicit distributional actor-critic (IDAC) built on two deep generator networks (DGNs) Semi-implicit actor (SIA) powered by a flexible policy distribution. We observe IDAC outperforms state-of-the-art algorithms on representative OpenAI Gym environments.
  arXiv  Detail & Related papers  (2020-07-13T02:52:18Z)
• Calibration of Shared Equilibria in General Sum Partially Observable Markov Games [15.572157454411533]
  We consider a general sum partially observable Markov game where agents of different types share a single policy network. This paper aims at i) formally understanding equilibria reached by such agents, and ii) matching emergent phenomena of such equilibria to real-world targets.
  arXiv  Detail & Related papers  (2020-06-23T15:14:20Z)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)
• Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning [55.20040781688844]
  QMIX is a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. We propose the StarCraft Multi-Agent Challenge (SMAC) as a new benchmark for deep multi-agent reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-19T16:51:51Z)
• Multi-Agent Interactions Modeling with Correlated Policies [53.38338964628494]
  In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework. We develop a Decentralized Adrial Imitation Learning algorithm with Correlated policies (CoDAIL) Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators.
  arXiv  Detail & Related papers  (2020-01-04T17:31:53Z)

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.