Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System

• URL: http://arxiv.org/abs/2407.02759v1
• Date: Wed, 3 Jul 2024 02:33:20 GMT
• Title: Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
• Authors: Yang Zhao, Chang Zhou, Jin Cao, Yi Zhao, Shaobo Liu, Chiyu Cheng, Xingchen Li,
• Abstract summary: We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales.
• Score: 38.501423778989704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.

Related papers

• 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)
• Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining [40.21546440726592]
  We propose a novel multi-agent collaborative data selection mechanism for large language models (LLMs) pretraining. In this framework, each data selection method serves as an independent agent, and an agent console is designed to dynamically integrate the information from all agents.
  arXiv  Detail & Related papers  (2024-10-10T16:45:28Z)
• Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization [63.554226552130054]
  Generalization poses a significant challenge in Multi-agent Reinforcement Learning (MARL) The extent to which an agent is influenced by unseen co-players depends on the agent's policy and the specific scenario. We present the Level of Influence (LoI), a metric quantifying the interaction intensity among agents within a given scenario and environment.
  arXiv  Detail & Related papers  (2023-10-11T06:09:26Z)
• Effective Multi-Agent Deep Reinforcement Learning Control with Relative Entropy Regularization [6.441951360534903]
  Multi-Agent Continuous Dynamic Policy Gradient (MACDPP) was proposed to tackle the issues of limited capability and sample efficiency in various scenarios controlled by multiple agents. It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure.
  arXiv  Detail & Related papers  (2023-09-26T07:38:19Z)
• Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning [139.53668999720605]
  We present a multi-agent PPO algorithm in which the local policy of each agent is updated similarly to vanilla PPO. We prove that with standard regularity conditions on the Markov game and problem-dependent quantities, our algorithm converges to the globally optimal policy at a sublinear rate.
  arXiv  Detail & Related papers  (2023-05-08T16:20:03Z)
• Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework [26.612749327414335]
  Decentralized execution is one core demand in cooperative multi-agent reinforcement learning (MARL) In this paper, we theoretically analyze two common classes of algorithms with decentralized policies -- multi-agent policy gradient methods and value-decomposition methods. We show that TAD-PPO can theoretically perform optimal policy learning in the finite multi-agent MDPs and shows significant outperformance on a large set of cooperative multi-agent tasks.
  arXiv  Detail & Related papers  (2022-07-12T06:59:13Z)
• Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach [128.62787284435007]
  We propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture. We prove that MF-PPO attains the globally optimal policy at a sublinear rate of convergence. In particular, we show that the inductive bias introduced by the permutation-invariant neural architecture enables MF-PPO to outperform existing competitors.
  arXiv  Detail & Related papers  (2021-05-18T04:35:41Z)
• Portfolio Search and Optimization for General Strategy Game-Playing [58.896302717975445]
  We propose a new algorithm for optimization and action-selection based on the Rolling Horizon Evolutionary Algorithm. For the optimization of the agents' parameters and portfolio sets we study the use of the N-tuple Bandit Evolutionary Algorithm. An analysis of the agents' performance shows that the proposed algorithm generalizes well to all game-modes and is able to outperform other portfolio methods.
  arXiv  Detail & Related papers  (2021-04-21T09:28:28Z)
• Multi-Agent Trust Region Policy Optimization [34.91180300856614]
  We show that the policy update of TRPO can be transformed into a distributed consensus optimization problem for multi-agent cases. We propose a decentralized MARL algorithm, which we call multi-agent TRPO (MATRPO)
  arXiv  Detail & Related papers  (2020-10-15T17:49:47Z)

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.