力学距離学習による半教師あり・教師なしスキル発見
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery
ゴールまでの期待時間を表す力学距離を学習し、報酬設計なしで複雑なタスクを効率的に学習する手法を提案。実機9自由度ハンドで画像入力と10個の選好ラベルのみからバルブ回しを習得した。
著者: Kristian Hartikainen, Xinyang Geng, Tuomas Haarnoja, Sergey Levine
分類: cs.LG, cs.AI, cs.CV, cs.RO, stat.ML
原文アブストラクト
Reinforcement learning requires manual specification of a reward function to learn a task. While in principle this reward function only needs to specify the task goal, in practice reinforcement learning can be very time-consuming or even infeasible unless the reward function is shaped so as to provide a smooth gradient towards a successful outcome. This shaping is difficult to specify by hand, particularly when the task is learned from raw observations, such as images. In this paper, we study how we can automatically learn dynamical distances: a measure of the expected number of time steps to reach a given goal state from any other state. These dynamical distances can be used to provide well-shaped reward functions for reaching new goals, making it possible to learn complex tasks efficiently. We show that dynamical distances can be used in a semi-supervised regime, where unsupervised interaction with the environment is used to learn the dynamical distances, while a small amount of preference supervision is used to determine the task goal, without any manually engineered reward function or goal examples. We evaluate our method both on a real-world robot and in simulation. We show that our method can learn to turn a valve with a real-world 9-DoF hand, using raw image observations and just ten preference labels, without any other supervision. Videos of the learned skills can be found on the project website: https://sites.google.com/view/dynamical-distance-learning.
