Neuron as an AgentDownload PDF

12 Feb 2018, 09:54 (edited 10 Feb 2022)ICLR 2018 Workshop SubmissionReaders: Everyone
  • Keywords: Multi-agent Reinforcement Learning, Communication, Reward Distribution, Trusted Third Party, Auction Theory
  • TL;DR: Neuron as an Agent (NaaA) enable us to train multi-agent communication without a trusted third party.
  • Abstract: Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments. This paper proposes reward distribution using {\em Neuron as an Agent} (NaaA) in MARL without a TTP with two key ideas: (i) inter-agent reward distribution and (ii) auction theory. Auction theory is introduced because inter-agent reward distribution is insufficient for optimization. Agents in NaaA maximize their profits (the difference between reward and cost) and, as a theoretical result, the auction mechanism is shown to have agents autonomously evaluate counterfactual returns as the values of other agents. NaaA enables representation trades in peer-to-peer environments, ultimately regarding unit in neural networks as agents. Finally, numerical experiments (a single-agent environment from OpenAI Gym and a multi-agent environment from ViZDoom) confirm that NaaA framework optimization leads to better performance in reinforcement learning.
  • Data: [OpenAI Gym](https://paperswithcode.com/dataset/openai-gym), [VizDoom](https://paperswithcode.com/dataset/vizdoom)
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