20
Jan
Test result: Mean-field Multi-agent Reinforcement Learning Platform
Zida Wu reinforcement learning; multi-agent; mean-field;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