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

• URL: http://arxiv.org/abs/2401.11666v1
• Date: Mon, 22 Jan 2024 02:58:53 GMT
• Title: P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer
• Authors: Zhiyuan Wang, Xiaoyang Qu, Jing Xiao, Bokui Chen, Jianzong Wang
• Abstract summary: Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model. 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.
• Score: 39.16560969128012
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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