Paper Target: IROS 2022

Current open source mean-field reinforcement learning code, which is based on MAgent

Blue: Group 1 (Proposed Algorithm)
Red: Group 2 (Opponent)

Algorihtm Information Collision Task
Current using all agents as neighborhood One state (position) only one agent Homogenous
Our proposed only using the closest neighborhood Allowing overlapping Heterogenous

Next Step

  • Step 1: Deal with information problem: using only closest neighborhood information
  • Step 2: Deal with collision problem: allowing overlapping
  • Step 3: Deal with task types problem: add heterogenous attributes.

Term illustration:

  • A round is the one batch training in multi-agent neural network. All algorithms are trained by 1599 rounds.
  • MF-AC: Mean-Field Actor-Critic algorithm
  • MF-Q: Mean-Field Q-learning algorithm
  • IL: Individual Q-learning algorithm
  • AC: Individual Actor-Critic algirhtm

All experimental results fit the results in original paper: Mean field multi-agent reinforcement learning, which should be MFQ>IL>MFAC>AC. Explanation could be founded in original paper.

MFAC Group VS AC Group

MFAC Group VS IL Group

MFAC Group VS MFQ Group

MFQ Group VS IL Group

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