Efficient RL via Disentangled Environment and Agent Representations
Kevin Gmelin Shikhar Bahl Russell Mendonca Deepak Pathak
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/