  Sharing parameters in multi-agent deep reinforcement learning has played an
essential role in allowing algorithms to scale to a large number of agents.
Parameter sharing between agents significantly decreases the number of
trainable parameters, shortening training times to tractable levels, and has
been linked to more efficient learning. However, having all agents share the
same parameters can also have a detrimental effect on learning. We demonstrate
the impact of parameter sharing methods on training speed and converged
returns, establishing that when applied indiscriminately, their effectiveness
is highly dependent on the environment. We propose a novel method to
automatically identify agents which may benefit from sharing parameters by
partitioning them based on their abilities and goals. Our approach combines the
increased sample efficiency of parameter sharing with the representational
capacity of multiple independent networks to reduce training time and increase
final returns.
