Papers › Multi-Task Reinforcement Learning with Soft Modularization

Multi-Task Reinforcement Learning with Soft Modularization

30 Mar 2020NeurIPS 2020 12arXiv:2003.13661archive 2025-07-28

Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang

Multi-task learning is a very challenging problem in reinforcement learning. While training multiple tasks jointly allow the policies to share parameters across different tasks, the optimization problem becomes non-trivial: It remains unclear what parameters in the network should be reused across tasks, and how the gradients from different tasks may interfere with each other. Thus, instead of naively sharing parameters across tasks, we introduce an explicit modularization technique on policy representation to alleviate this optimization issue. Given a base policy network, we design a routing network which estimates different routing strategies to reconfigure the base network for each task. Instead of directly selecting routes for each task, our task-specific policy uses a method called soft modularization to softly combine all the possible routes, which makes it suitable for sequential tasks. We experiment with various robotics manipulation tasks in simulation and show our method improves both sample efficiency and performance over strong baselines by a large margin.

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RchalYang/Soft-Module mentioned on GitHubpytorch report

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Meta-Learning Multi-Task Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Meta-Learning	MT50	SoftModule	Average Success Rate	60.0%	#1 of 4	Archive leaderboard	report

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