真の状態なしで強化学習を行う手法
Reinforcement Learning without Ground-Truth State
目標観測との一致のみを報酬とする指標報酬関数と、達成目標による再ラベリングを組み合わせ、真の状態推定なしにRGB-D画像からロープ操作などの複雑な連続状態空間タスクを学習できることを示した。
著者: Xingyu Lin, Harjatin Singh Baweja, David Held
分類: cs.RO, cs.LG
原文アブストラクト
To perform robot manipulation tasks, a low-dimensional state of the environment typically needs to be estimated. However, designing a state estimator can sometimes be difficult, especially in environments with deformable objects. An alternative is to learn an end-to-end policy that maps directly from high-dimensional sensor inputs to actions. However, if this policy is trained with reinforcement learning, then without a state estimator, it is hard to specify a reward function based on high-dimensional observations. To meet this challenge, we propose a simple indicator reward function for goal-conditioned reinforcement learning: we only give a positive reward when the robot's observation exactly matches a target goal observation. We show that by relabeling the original goal with the achieved goal to obtain positive rewards (Andrychowicz et al., 2017), we can learn with the indicator reward function even in continuous state spaces. We propose two methods to further speed up convergence with indicator rewards: reward balancing and reward filtering. We show comparable performance between our method and an oracle which uses the ground-truth state for computing rewards. We show that our method can perform complex tasks in continuous state spaces such as rope manipulation from RGB-D images, without knowledge of the ground-truth state.
