Agents that are aware of the separation between themselves and their environments can leverage this understanding to form effective representations of visual input. We propose an approach for learning such structured representations for RL algorithms, using visual knowledge of the agent, such as its shape or mask, which is often inexpensive to obtain. This is incorporated into the RL objective using a simple auxiliary loss. We show that our method, Structured Environment-Agent Representations, outperforms state-of-the-art model-free approaches over 18 different challenging visual simulation environments spanning 5 different robots. Website at https://sear-rl.github.io/

翻译：意识到自身与环境之间区别的智能体可以利用这一理解形成视觉输入的有效表征。我们提出一种为强化学习算法学习此类结构化表征的方法，该方法利用智能体的视觉知识（如其形状或掩码，这些知识通常获取成本低廉），并通过简单辅助损失将其整合到强化学习目标中。实验表明，我们的方法——结构化环境-智能体表征——在跨越5种不同机器人的18个具有挑战性的视觉模拟环境中，优于最先进的无模型方法。详见网址https://sear-rl.github.io/