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

Efficient RL via Disentangled Environment and Agent Representations