A PHP Agency brochure promoting the company calculates that even a part-time associate could make more than $23,000, while noting in tiny print below that the income figures are "hypothetical and are not based on actual results." Multilevel marketing companies rely on recruiting endless new chains of subordinate sales associates, according to Brooks, meaning that people who have already paid to join the company are increasing their own pool of competition. People will say, ‘Well, I don’t really understand this business, but if Mr. “They find some celebrity endorser who will speak at a convention, so that gives them the air of legitimacy. “That’s a big game the companies play,” said Douglas Brooks, an attorney who has filed lawsuits against multilevel marketing companies and who is a board member of watchdog group Pyramid Scheme Alert. “Think about the most regulated industry out there, it’s this industry,” Bet-David said.īy lending their reputations to PHP Agency, experts say celebrities like Bush and Peterson are bolstering the business’s legitimacy in the eyes of recruits. “There’s nothing about our model that’s multilevel marketing,” Bet-David said.īet-David denied that his company participated in the practices that former sales associates complained about to the Better Business Bureau, citing industry regulations. A Reddit forum devoted to discussions of multilevel marketing companies is filled with similar horror stories about the company, with tales of PHP agents pressuring friends and family members to pay initiation fees to join the company and savings squandered on PHP Agency trainings.īet-David, PHP’s CEO, told The Daily Beast that his company doesn’t do multilevel marketing.
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Multiple people complained that someone had persuaded them to join their insurance team and pay $150 for “training material,” plus a monthly recurring fee of $14.95. Its PHP page is littered with complaints. The poster features the celebrities arranged around PHP Agency’s CEO, Patrick Bet-David, a star in YouTube’s business motivation community who has amassed more than 1 million followers on his channel.īut much of PHP’s income appears to come from fees paid by the recruits themselves, according to complaints filed with the Better Business Bureau. The conference lineup became public through the poster announcing their appearance, which features a logo that’s nearly identical to the symbol for the Autobots, the good robots from Transformers. Representatives for the speakers didn’t respond to requests for comment. They’ll be joined by Oakland Athletics executive Billy Beane, who inspired the Michael Lewis book Moneyball. They are scheduled to speak in late July at the People Helping People Conference, an annual event organized by insurance sales company PHP Agency.
Bush, basketball star Kobe Bryant, and right-wing Canadian professor Jordan Peterson will lend their famous names this summer to a multilevel insurance marketing company that critics say is better at getting money from its own recruits than it is at selling insurance. Empirical evaluations on the StarCraft II micromanagement benchmark demonstrate that, compared with state-of-the-art MARL algorithms, the SR-MARL framework improves the average test win rate by 0.17%, 6.08%, and 3.24%, and reduces the deviation by 16.67%, 30.80%, and 66.30%, under easy, hard, and super hard scenarios.Former President George W. The SIRD is agnostic to specific MARL algorithms and flexibly integrated with various value function factorization approaches. Specifically, the SIRD consists of structuralization, sparsification, and optimization modules, where an optimal encoding tree is generated to perform abstracting to discover roles. The SIRD transforms role discovery into a hierarchical action space clustering. In this article, we propose a mathematical Structural Information principles-based Role Discovery method, namely SIRD, and then present a SIRD optimizing MARL framework, namely SR-MARL, for multi-agent collaboration.
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Nevertheless, without manual assistance, current role-based methods cannot guarantee stably discovering a set of roles to effectively decompose a complex task, as they assume either a predefined role structure or practical experience for selecting hyperparameters. Role-based learning is a promising approach to improving the performance of Multi-Agent Reinforcement Learning (MARL). MAS: Multiagent Learning, ML: Reinforcement Learning Algorithms Abstract
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