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First, the long short-term memory (LSTM) model is utilized to strengthen financial investment data. Then, the hidden Markov model (HMM) is utilized to make a preliminary judgment on financial investment strategy. Next, a multi-agent deep deterministic policy gradient (MADDPG) is employed to set the goal of agent reinforcement and determine decision indicators based on the financial market environment. Multiple agents (investors) form a cooperative strategy through learning and interaction to achieve the optimal investment decision in the financial market. The experimental results on realistic data indicate that the proposal can play an important role in investment decision-making. <\/jats:p>","DOI":"10.1142\/s0218126624502669","type":"journal-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T09:13:50Z","timestamp":1713431630000},"source":"Crossref","is-referenced-by-count":3,"title":["An Intelligent Financial Investment Decision Model Based on Multi-Agent Reinforcement Learning"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4336-4295","authenticated-orcid":false,"given":"Qi","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Business Administration, University of Illinois Urbana-Champaign, Champaign, Illinois 61820, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8724-4352","authenticated-orcid":false,"given":"Bohan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Illinois Chicago, Chicago, Illinois 60607, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"S0218126624502669BIB001","first-page":"1","volume":"8","author":"Guo Z.","year":"2023","journal-title":"IEEE Trans. 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