Learning by Playing Solving Sparse Reward Tasks from Scratch

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch - in the presence of multiple sparse reward signals. To this end, the agent is equipped with a set of general auxiliary tasks, that it attempts to learn simultaneously via off-policy RL. The key idea behind our method is that active (learned) scheduling and execution of auxiliary policies allows the agent to efficiently explore its environment - enabling it to excel at sparse reward RL. Our experiments in several challenging robotic manipulation settings demonstrate the power of our approach.

Authors

• Martin A. Riedmiller
• Roland Hafner
• Thomas Lampe
• Michael Neunert
• Jonas Degrave
• Tom Van de Wiele
• Volodymyr Mnih
• Nicolas Heess
• Jost Tobias Springenberg

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