  We study the problem of learning control policies for complex tasks given by
logical specifications. Recent approaches automatically generate a reward
function from a given specification and use a suitable reinforcement learning
algorithm to learn a policy that maximizes the expected reward. These
approaches, however, scale poorly to complex tasks that require high-level
planning. In this work, we develop a compositional learning approach, called
DiRL, that interleaves high-level planning and reinforcement learning. First,
DiRL encodes the specification as an abstract graph; intuitively, vertices and
edges of the graph correspond to regions of the state space and simpler
sub-tasks, respectively. Our approach then incorporates reinforcement learning
to learn neural network policies for each edge (sub-task) within a
Dijkstra-style planning algorithm to compute a high-level plan in the graph. An
evaluation of the proposed approach on a set of challenging control benchmarks
with continuous state and action spaces demonstrates that it outperforms
state-of-the-art baselines.
