汎用計算グラフを用いた自己教師あり深層強化学習によるロボットナビゲーション
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
モデルフリーとモデルベースの中間を取る汎用計算グラフを提案し、生画像からサンプル効率よくナビゲーションを学習する手法を実現した。実機RCカーで数時間の自己教師あり学習により複雑な屋内環境の走行を達成した。
著者: Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to navigate through the internal map. However, these approaches often include a variety of assumptions, are computationally intensive, and do not learn from failures. In contrast, learning-based methods improve as the robot acts in the environment, but are difficult to deploy in the real-world due to their high sample complexity. To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based. We then instantiate this graph to form a navigation model that learns from raw images and is sample efficient. Our simulated car experiments explore the design decisions of our navigation model, and show our approach outperforms single-step and $N$-step double Q-learning. We also evaluate our approach on a real-world RC car and show it can learn to navigate through a complex indoor environment with a few hours of fully autonomous, self-supervised training. Videos of the experiments and code can be found at github.com/gkahn13/gcg
