  In a multi-agent setting, the optimal policy of a single agent is largely
dependent on the behavior of other agents. We investigate the problem of
multi-agent reinforcement learning, focusing on decentralized learning in
non-stationary domains for mobile robot navigation. We identify a cause for the
difficulty in training non-stationary policies: mutual adaptation to
sub-optimal behaviors, and we use this to motivate a curriculum-based strategy
for learning interactive policies. The curriculum has two stages. First, the
agent leverages policy gradient algorithms to learn a policy that is capable of
achieving multiple goals. Second, the agent learns a modifier policy to learn
how to interact with other agents in a multi-agent setting. We evaluated our
approach on both an autonomous driving lane-change domain and a robot
navigation domain.

Artificial Intelligence;Machine Learning