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Talk
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Irina Higgins · Arka Pal · Andrei Rusu · Loic Matthey · Christopher Burgess · Alexander Pritzel · Matthew Botvinick · Charles Blundell · Alexander Lerchner

Sun Aug 06 11:42 PM -- 12:00 AM (PDT) @ C4.5
in Reinforcement learning 3 »

Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, 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 domain. 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 disentangled representation of the observed environment. 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 scenarios, an effect that holds across a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms (DQN, A3C and EC).

Author Information

Irina Higgins (DeepMind)
Irina Higgins

Irina Higgins is a research scientist at DeepMind, where she works in the Froniers team. Her work aims to bring together insights from the fields of neuroscience and physics to advance general artificial intelligence through improved representation learning. Before joining DeepMind, Irina was a British Psychological Society Undergraduate Award winner for her achievements as an undergraduate student in Experimental Psychology at Westminster University, followed by a DPhil at the Oxford Centre for Computational Neuroscience and Artificial Intelligence, where she focused on understanding the computational principles underlying speech processing in the auditory brain. During her DPhil, Irina also worked on developing poker AI, applying machine learning in the finance sector, and working on speech recognition at Google Research.

Arka Pal (DeepMind)
Andrei Rusu (DeepMind)
Loic Matthey (DeepMind)
Christopher Burgess (DeepMind)
Alexander Pritzel (Deepmind)
Matthew Botvinick (DeepMind)
Charles Blundell (DeepMind)
Alexander Lerchner (DeepMind)

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