  Efficient exploration in deep cooperative multi-agent reinforcement learning
(MARL) still remains challenging in complex coordination problems. In this
paper, we introduce a novel Episodic Multi-agent reinforcement learning with
Curiosity-driven exploration, called EMC. We leverage an insight of popular
factorized MARL algorithms that the "induced" individual Q-values, i.e., the
individual utility functions used for local execution, are the embeddings of
local action-observation histories, and can capture the interaction between
agents due to reward backpropagation during centralized training. Therefore, we
use prediction errors of individual Q-values as intrinsic rewards for
coordinated exploration and utilize episodic memory to exploit explored
informative experience to boost policy training. As the dynamics of an agent's
individual Q-value function captures the novelty of states and the influence
from other agents, our intrinsic reward can induce coordinated exploration to
new or promising states. We illustrate the advantages of our method by didactic
examples, and demonstrate its significant outperformance over state-of-the-art
MARL baselines on challenging tasks in the StarCraft II micromanagement
benchmark.
