Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms

By Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht. 2020

33 citations recorded by the source corpus.

Subfields: Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. Yu Wang
2. Alexandre M. Bayen
3. Chao Yu
4. Eugene Vinitsky
5. Akash Velu
6. Yi Wu
7. Stefano V. Albrecht
8. Filippos Christianos

Citations in the corpus

• The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games (2021, methodology): “[24] perform a broad benchmark of various MARL algorithms and note that PPO-based methods often perform competitively to other methods.”
• Local Information Agent Modelling in Partially-Observable Environments (2021, methodology): “We evaluate LIAM in three different multi-agent environments: double speaker-listener [Mordatch and Abbeel, 2017, Lowe et al., 2017], level-based foraging (LBF) [Albrecht and Stone, 2017, Papoudakis et al., 2020], and a modified version of predator-prey proposed by [Böhmer et al., 2020].”
• Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms on a Building Energy Demand Coordination Task (2020, background): “This might be because centralized agents have trouble coordinating as a result of the lazy-agent problem [8].”