DoorGym: スケーラブルなドア開け環境とベースラインエージェント
DoorGym: A Scalable Door Opening Environment And Baseline Agent
ドメインランダム化を用いてドア開けタスクの汎化方策を訓練できるオープンソースのシミュレーション環境DoorGymを提案し、PPOとSACのベースライン実装および実世界への転移実験を示した。
著者: Yusuke Urakami, Alec Hodgkinson, Casey Carlin, Randall Leu, Luca Rigazio, Pieter Abbeel
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
In order to practically implement the door opening task, a policy ought to be robust to a wide distribution of door types and environment settings. Reinforcement Learning (RL) with Domain Randomization (DR) is a promising technique to enforce policy generalization, however, there are only a few accessible training environments that are inherently designed to train agents in domain randomized environments. We introduce DoorGym, an open-source door opening simulation framework designed to utilize domain randomization to train a stable policy. We intend for our environment to lie at the intersection of domain transfer, practical tasks, and realism. We also provide baseline Proximal Policy Optimization and Soft Actor-Critic implementations, which achieves success rates between 0% up to 95% for opening various types of doors in this environment. Moreover, the real-world transfer experiment shows the trained policy is able to work in the real world. Environment kit available here: https://github.com/PSVL/DoorGym/
