  The composition of elementary behaviors to solve challenging transfer
learning problems is one of the key elements in building intelligent machines.
To date, there has been plenty of work on learning task-specific policies or
skills but almost no focus on composing necessary, task-agnostic skills to find
a solution to new problems. In this paper, we propose a novel deep
reinforcement learning-based skill transfer and composition method that takes
the agent's primitive policies to solve unseen tasks. We evaluate our method in
difficult cases where training policy through standard reinforcement learning
(RL) or even hierarchical RL is either not feasible or exhibits high sample
complexity. We show that our method not only transfers skills to new problem
settings but also solves the challenging environments requiring both task
planning and motion control with high data efficiency.
