We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several baselines.

介绍了一种结合了model-free和model-based特点的deep reinforcement learning方法——Imagination-Augmented Agents（I2As），相比于现有的model-based基于规则的reinforcement learning和planning方法，I2As通过学习来解释环境模型的预测，以任意方式构建隐式计划，使用预测作为深度策略网络中的额外上下文，相比于基线算法，在数据效率，性能和鲁棒性方面获得了改进。

深度强化学习中的想象增强智能体