Logical Team Q-learning: An approach towards factored policies in cooperative MARL
We address the challenge of learning factored policies in cooperative MARL scenarios. In particular, we consider the situation in which a team of agents collaborates to optimize a common cost. The goal is to obtain factored policies that determine the individual behavior of each agent so that the resulting joint policy is optimal. The main contribution of this work is the introduction of Logical Team Q-learning (LTQL). LTQL does not rely on assumptions about the environment and hence is generally applicable to any collaborative MARL scenario. We derive LTQL as a stochastic approximation to a dynamic programming method we introduce in this work. We conclude the paper by providing experiments (both in the tabular and deep settings) that illustrate the claims.
WOS:000659893800075
2021-01-01
Brookline
Proceedings of Machine Learning Research; 130
667
675
REVIEWED
Event name | Event place | Event date |
ELECTR NETWORK | Apr 13-15, 2021 | |