  Standard model-free deep reinforcement learning (RL) algorithms sample a new
initial state for each trial, allowing them to optimize policies that can
perform well even in highly stochastic environments. However, problems that
exhibit considerable initial state variation typically produce high-variance
gradient estimates for model-free RL, making direct policy or value function
optimization challenging. In this paper, we develop a novel algorithm that
instead partitions the initial state space into "slices", and optimizes an
ensemble of policies, each on a different slice. The ensemble is gradually
unified into a single policy that can succeed on the whole state space. This
approach, which we term divide-and-conquer RL, is able to solve complex tasks
where conventional deep RL methods are ineffective. Our results show that
divide-and-conquer RL greatly outperforms conventional policy gradient methods
on challenging grasping, manipulation, and locomotion tasks, and exceeds the
performance of a variety of prior methods. Videos of policies learned by our
algorithm can be viewed at http://bit.ly/dnc-rl
