Composing Task-Agnostic Policies with Deep Reinforcement Learning
This addresses the challenge of transfer learning in AI by enabling efficient composition of primitive skills for new problems, though it appears incremental as it builds on existing deep reinforcement learning methods.
The paper tackles the problem of composing task-agnostic policies to solve unseen tasks in reinforcement learning, showing that the method transfers skills and solves challenging environments with high data efficiency.
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.