環境とエージェントの表現を分離した効率的強化学習
Efficient RL via Disentangled Environment and Agent Representations
エージェント自身の形状やマスク情報を補助損失として活用し、環境とエージェントの表現を分離して学習することで、視覚ベースの強化学習を効率化する手法を提案した。
著者: Kevin Gmelin, Shikhar Bahl, Russell Mendonca, Deepak Pathak
分類: cs.LG, cs.AI, cs.CV, cs.NE, cs.RO
原文アブストラクト
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/
