用Transformer增强DreamerV3，提升复杂环境下的决策能力。
TransDreamerV3: Implanting Transformer In DreamerV3
- 在DreamerV3中加入Transformer编码器，增强记忆与状态理解。
- 在Atari-Freeway和Crafter任务上表现优于原版DreamerV3。
- 适合研究世界模型与自回归策略的RL方向学者参考。
本文提出TransDreamerV3，通过在DreamerV3架构中引入Transformer编码器，提升复杂环境中的记忆与决策能力。在Atari-Boxing、Atari-Freeway、Atari-Pong和Crafter任务上进行了实验，结果显示该模型在Atari-Freeway和Crafter任务中性能优于原始DreamerV3。尽管在Minecraft任务中存在问题，且部分任务训练时间有限，但整体表明基于世界模型的强化学习可借助Transformer架构实现显著进步。
原文摘要 · Abstract (English)
This paper introduces TransDreamerV3, a reinforcement learning model that enhances the DreamerV3 architecture by integrating a transformer encoder. The model is designed to improve memory and decision-making capabilities in complex environments. We conducted experiments on Atari-Boxing, Atari-Freeway, Atari-Pong, and Crafter tasks, where TransDreamerV3 demonstrated improved performance over DreamerV3, particularly in the Atari-Freeway and Crafter tasks. While issues in the Minecraft task and limited training across all tasks were noted, TransDreamerV3 displays advancement in world model-based reinforcement learning, leveraging transformer architectures.
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