  Deep reinforcement learning (DRL) is a very active research area. However,
several technical and scientific issues require to be addressed, amongst which
we can mention data inefficiency, exploration-exploitation trade-off, and
multi-task learning. Therefore, distributed modifications of DRL were
introduced; agents that could be run on many machines simultaneously. In this
article, we provide a survey of the role of the distributed approaches in DRL.
We overview the state of the field, by studying the key research works that
have a significant impact on how we can use distributed methods in DRL. We
choose to overview these papers, from the perspective of distributed learning,
and not the aspect of innovations in reinforcement learning algorithms. Also,
we evaluate these methods on different tasks and compare their performance with
each other and with single actor and learner agents.
