Deep Multiagent Reinforcement Learning: Challenges and Directions
It addresses the complexity of scaling reinforcement learning to multiagent systems for researchers and practitioners, but is incremental as a survey.
This paper surveys deep multiagent reinforcement learning, identifying key challenges like computational complexity and nonstationarity, and suggests future research should adopt an interdisciplinary approach to address these issues.
This paper surveys the field of deep multiagent reinforcement learning. The combination of deep neural networks with reinforcement learning has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments. Dealing with multiple agents is inherently more complex as (a) the future rewards depend on multiple players' joint actions and (b) the computational complexity increases. We present the most common multiagent problem representations and their main challenges, and identify five research areas that address one or more of these challenges: centralised training and decentralised execution, opponent modelling, communication, efficient coordination, and reward shaping. We find that many computational studies rely on unrealistic assumptions or are not generalisable to other settings; they struggle to overcome the curse of dimensionality or nonstationarity. Approaches from psychology and sociology capture promising relevant behaviours, such as communication and coordination, to help agents achieve better performance in multiagent settings. We suggest that, for multiagent reinforcement learning to be successful, future research should address these challenges with an interdisciplinary approach to open up new possibilities in multiagent reinforcement learning.