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arXiv · 2401.11666

P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer

Zhiyuan Wang·Xiaoyang Qu·Jing Xiao·Bokui Chen·Jianzong Wang
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Abstract

Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance degradation when these agents face new tasks. In our work, we propose a novel solution - the Progressive Prompt Decision Transformer (P2DT). This method enhances a transformer-based model by dynamically appending decision tokens during new task training, thus fostering task-specific policies. Our approach mitigates forgetting in continual and offline reinforcement learning scenarios. Moreover, P2DT leverages trajectories collected via traditional reinforcement learning from all tasks and generates new task-specific tokens during training, thereby retaining knowledge from previous studies. Preliminary results demonstrate that our model effectively alleviates catastrophic forgetting and scales well with increasing task environments.

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Zhiyuan Wang, Xiaoyang Qu, Jing Xiao, Bokui Chen, Jianzong Wang. 2024-01-22. P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer. https://arxiv.org/abs/2401.11666

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