We present several new algorithms for multiagent
reinforcement learning. A common feature of these
algorithms is a parameterized, structured representation
of a policy or value function. This structure
is leveraged in an approach we call coordinated reinforcement
learning, by which agents coordinate
both their action selection activities and their parameter
updates. Within the limits of our parametric
representations, the agents will determine
a jointly optimal action without explicitly considering
every possible action in their exponentially
large joint action space. Our methods differ from
many previous reinforcement learning approaches
to multiagent coordination in that structured communication
and coordination between agents appears
at the core of both the learning algorithm and
the execution architecture. Our experimental results,
comparing our approach to other RL methods,
illustrate both the quality of the policies obtained
and the additional benefits of coordination.
(EN)
