paper： DARLA: Improving Zero-Shot Transfer in DRL

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

DARLA (DisentAngled Representation Learning Agent)

Abstract

Domain adaptation is an important open prob- lem in deep reinforcement learning (RL). In many scenarios of interest data is hard to ob- tain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target do- main. We propose a new multi-stage RL agent, DARLA (DisentAngled Representation Learning Agent), which learns to see before learning to act. DARLA’s vision is based on learning a disen- tangled representation of the observed environ- ment. Once DARLA can see, it is able to acquire source policies that are robust to many domain shifts - even with no access to the target domain. DARLA significantly outperforms conventional baselines in zero-shot domain adaptation scenar- ios, an effect that holds across a variety of RL en- vironments (Jaco arm, DeepMind Lab) and base RL algorithms (DQN, A3C and EC).

https://arxiv.org/abs/1707.08475