  In this paper, we consider the problem of building learning agents that can
efficiently learn to navigate in constrained environments. The main goal is to
design agents that can efficiently learn to understand and generalize to
different environments using high-dimensional inputs (a 2D map), while
following feasible paths that avoid obstacles in obstacle-cluttered
environment. To achieve this, we make use of traditional path planning
algorithms, supervised learning, and reinforcement learning algorithms in a
synergistic way. The key idea is to decouple the navigation problem into
planning and control, the former of which is achieved by supervised learning
whereas the latter is done by reinforcement learning. Specifically, we train a
deep convolutional network that can predict collision-free paths based on a map
of the environment-- this is then used by a reinforcement learning algorithm to
learn to closely follow the path. This allows the trained agent to achieve good
generalization while learning faster. We test our proposed method in the
recently proposed Safety Gym suite that allows testing of safety-constraints
during training of learning agents. We compare our proposed method with
existing work and show that our method consistently improves the sample
efficiency and generalization capability to novel environments.
