Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning

• URL: http://arxiv.org/abs/2111.08010v1
• Date: Mon, 15 Nov 2021 12:31:31 GMT
• Title: Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning
• Authors: Robin Schiewer and Laurenz Wiskott
• Abstract summary: We study whether model-based multi-task reinforcement learning benefits from shared dynamics models in a similar way model-free methods do from shared policy networks. Using a single dynamics model, we see clear evidence of task confusion and reduced performance. As a remedy, enforcing an internal structure for the learned dynamics model by training isolated sub-networks for each task notably improves performance.
• Score: 0.8883733362171032
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a multi-task reinforcement learning setting, the learner commonly benefits from training on multiple related tasks by exploiting similarities among them. At the same time, the trained agent is able to solve a wider range of different problems. While this effect is well documented for model-free multi-task methods, we demonstrate a detrimental effect when using a single learned dynamics model for multiple tasks. Thus, we address the fundamental question of whether model-based multi-task reinforcement learning benefits from shared dynamics models in a similar way model-free methods do from shared policy networks. Using a single dynamics model, we see clear evidence of task confusion and reduced performance. As a remedy, enforcing an internal structure for the learned dynamics model by training isolated sub-networks for each task notably improves performance while using the same amount of parameters. We illustrate our findings by comparing both methods on a simple gridworld and a more complex vizdoom multi-task experiment.

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