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

Composing Task-Agnostic Policies with Deep Reinforcement Learning