Projected Task-Specific Layers for Multi-Task Reinforcement Learning
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Abstract (arXiv)
Multi-task reinforcement learning could enable robots to scale across a wide variety of manipulation tasks in homes and workplaces. However, generalizing from one task to another and mitigating negative task interference still remains a challenge. Addressing this challenge by successfully sharing information across tasks will depend on how well the structure underlying the tasks is captured. In this work, we introduce our new architecture, Projected Task-Specific Layers (PTSL), that leverages a common policy with dense task-specific corrections through task-specific layers to better express shared and variable task information. We then show that our model outperforms the state of the art on the MT10 and MT50 benchmarks of Meta-World consisting of 10 and 50 goal-conditioned tasks for a Sawyer arm.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 31 references that are in the map. First posted on arXiv 15 Sep 2023.
Builds on
21 of 31 references.
References · 21
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9 more
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