Kuba, JG;
Wen, M;
Meng, L;
Gu, S;
Zhang, H;
Mguni, DH;
Wang, J;
(2021)
Settling the Variance of Multi-Agent Policy Gradients.
In:
Advances in Neural Information Processing Systems 34 (NeurIPS 2021).
(pp. pp. 13458-13470).
NeurIPS Proceedings
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Abstract
Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectiveness of multi-agent PG (MAPG) methods degrades as the variance of gradient estimates increases rapidly with the number of agents. In this paper, we offer a rigorous analysis of MAPG methods by, firstly, quantifying the contributions of the number of agents and agents’ explorations to the variance of MAPG estimators. Based on this analysis, we derive the optimal baseline (OB) that achieves the minimal variance. In comparison to the OB, we measure the excess variance of existing MARL algorithms such as vanilla MAPG and COMA. Considering using deep neural networks, we also propose a surrogate version of OB, which can be seamlessly plugged into any existing PG methods in MARL. On benchmarks of Multi-Agent MuJoCo and StarCraft challenges, our OB technique effectively stabilises training and improves the performance of multi-agent PPO and COMA algorithms by a significant margin. Code is released at https://github.com/morning9393/Optimal-Baseline-for-Multi-agent-Policy-Gradients.
| Type: | Proceedings paper |
|---|---|
| Title: | Settling the Variance of Multi-Agent Policy Gradients |
| Event: | 35th Conference on Neural Information Processing Systems (NeurIPS 2021). |
| ISBN-13: | 9781713845393 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.neurips.cc/paper/2021 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10154101 |
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