Train An Imaginative Agent· 训练想象代理
A seemingly reasonable push in Sokoban can make the puzzle unsolvable. To avoid such mistakes, an agent has to anticipate the consequences of its actions before execution, especially when feedback from the real environment is sparse or actions are irreversible. We introduce Agentic Imagination (Agima), a training framework that trains an agent to imagine before acting, i.e. to be imaginative. Agima trains a single omni model to propose actions, generate images of their outcomes, and then reason over these imagined futures to decide what to execute in the real environment. Rather than committing to a single plan, the agent can simulate a sequence of actions, explore alternatives, and compare possible futures. We first use supervised fine-tuning (SFT) to unify policy and world modeling withi
提出Agima框架，使代理能够在行动前进行视觉想象，提高决策质量。
- 核心方法
- Agima通过训练单个全功能模型来提出动作、生成结果图像，并基于这些想象的未来情景做出决策。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 在环境反馈稀疏或动作不可逆的情况下，代理需要在行动前预测动作后果，避免导致问题不可解的情况。
- 关键实验
- 在Sokoban等环境中的实验展示了Agima的有效性，但具体实验细节未提供。
- 主要贡献
- 引入了代理视觉想象力的概念，使代理能模拟动作序列、探索多种方案及比较可能的结果。
- 意义与局限
- 提高了代理在复杂环境中的决策能力，但计算成本和模型复杂度可能较高。