• Corpus ID: 12099821

Multi-Advisor Reinforcement Learning

@article{Laroche2017MultiAdvisorRL,
  title={Multi-Advisor Reinforcement Learning},
  author={Romain Laroche and Mehdi Fatemi and Joshua Romoff and Harm van Seijen},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.00756}
}
We consider tackling a single-agent RL problem by distributing it to $n$ learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system. We show that the local planning method for the advisors is critical and that none of the ones found in the literature is flawless: the egocentric planning overestimates values of states where the other… 

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