论文标题
使用深层生成模型对自主剂的能力评估
Competency Assessment for Autonomous Agents using Deep Generative Models
论文作者
论文摘要
为了使自主代理人充当人类用户的值得信赖的合作伙伴，他们必须能够可靠地传达他们要求执行的任务的能力。为了实现这一目标，我们基于深层生成建模开发了概率世界模型，以模拟代理轨迹并准确计算任务结果概率。通过将条件变分自动编码器的优势与复发性神经网络相结合，深层生成世界模型可以从长范围内预测到任务完成。我们展示了这些预测的轨迹如何用于计算结果概率分布，从而可以精确评估特定任务和初始设置的代理能力。
For autonomous agents to act as trustworthy partners to human users, they must be able to reliably communicate their competency for the tasks they are asked to perform. Towards this objective, we develop probabilistic world models based on deep generative modelling that allow for the simulation of agent trajectories and accurate calculation of tasking outcome probabilities. By combining the strengths of conditional variational autoencoders with recurrent neural networks, the deep generative world model can probabilistically forecast trajectories over long horizons to task completion. We show how these forecasted trajectories can be used to calculate outcome probability distributions, which enable the precise assessment of agent competency for specific tasks and initial settings.
