803 research outputs found
Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches
This paper surveys the field of multiagent deep 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 the joint actions
of multiple players and (b) the computational complexity of functions
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.
We suggest that, for multiagent reinforcement learning to be successful, future
research addresses these challenges with an interdisciplinary approach to open
up new possibilities for more human-oriented solutions in multiagent
reinforcement learning.Comment: 37 pages, 6 figure
- …