  We study the problem of cooperative multi-agent reinforcement learning with a
single joint reward signal. This class of learning problems is difficult
because of the often large combined action and observation spaces. In the fully
centralized and decentralized approaches, we find the problem of spurious
rewards and a phenomenon we call the "lazy agent" problem, which arises due to
partial observability. We address these problems by training individual agents
with a novel value decomposition network architecture, which learns to
decompose the team value function into agent-wise value functions. We perform
an experimental evaluation across a range of partially-observable multi-agent
domains and show that learning such value-decompositions leads to superior
results, in particular when combined with weight sharing, role information and
information channels.
