  State-of-the-art meta reinforcement learning algorithms typically assume the
setting of a single agent interacting with its environment in a sequential
manner. A negative side-effect of this sequential execution paradigm is that,
as the environment becomes more and more challenging, and thus requiring more
interaction episodes for the meta-learner, it needs the agent to reason over
longer and longer time-scales. To combat the difficulty of long time-scale
credit assignment, we propose an alternative parallel framework, which we name
"Concurrent Meta-Reinforcement Learning" (CMRL), that transforms the temporal
credit assignment problem into a multi-agent reinforcement learning one. In
this multi-agent setting, a set of parallel agents are executed in the same
environment and each of these "rollout" agents are given the means to
communicate with each other. The goal of the communication is to coordinate, in
a collaborative manner, the most efficient exploration of the shared task the
agents are currently assigned. This coordination therefore represents the
meta-learning aspect of the framework, as each agent can be assigned or assign
itself a particular section of the current task's state space. This framework
is in contrast to standard RL methods that assume that each parallel rollout
occurs independently, which can potentially waste computation if many of the
rollouts end up sampling the same part of the state space. Furthermore, the
parallel setting enables us to define several reward sharing functions and
auxiliary losses that are non-trivial to apply in the sequential setting. We
demonstrate the effectiveness of our proposed CMRL at improving over sequential
methods in a variety of challenging tasks.
