  There is a growing interest in Multi-Agent Reinforcement Learning (MARL) as
the first steps towards building general intelligent agents that learn to make
low and high-level decisions in non-stationary complex environments in the
presence of other agents. Previous results point us towards increased
conditions for coordination, efficiency/fairness, and common-pool resource
sharing. We further study coordination in multi-task environments where several
rewarding tasks can be performed and thus agents don't necessarily need to
perform well in all tasks, but under certain conditions may specialize. An
observation derived from the study is that epsilon greedy exploration of
value-based reinforcement learning methods is not adequate for multi-agent
independent learners because the epsilon parameter that controls the
probability of selecting a random action synchronizes the agents artificially
and forces them to have deterministic policies at the same time. By using
policy-based methods with independent entropy regularised exploration updates,
we achieved a better and smoother convergence. Another result that needs to be
further investigated is that with an increased number of agents specialization
tends to be more probable.
