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Tobias Pohlen

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2 papers
2 author rows

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2

ICML Conference 2022 Conference Paper

A data-driven approach for learning to control computers

  • Peter Conway Humphreys
  • David Raposo
  • Tobias Pohlen
  • Gregory Thornton
  • Rachita Chhaparia
  • Alistair Muldal
  • Josh Abramson
  • Petko Georgiev

It would be useful for machines to use computers as humans do so that they can aid us in everyday tasks. This is a setting in which there is also the potential to leverage large-scale expert demonstrations and human judgements of interactive behaviour, which are two ingredients that have driven much recent success in AI. Here we investigate the setting of computer control using keyboard and mouse, with goals specified via natural language. Instead of focusing on hand-designed curricula and specialized action spaces, we focus on developing a scalable method centered on reinforcement learning combined with behavioural priors informed by actual human-computer interactions. We achieve state-of-the-art and human-level mean performance across all tasks within the MiniWob++ benchmark, a challenging suite of computer control problems, and find strong evidence of cross-task transfer. These results demonstrate the usefulness of a unified human-agent interface when training machines to use computers. Altogether our results suggest a formula for achieving competency beyond MiniWob++ and towards controlling computers, in general, as a human would.

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NeurIPS Conference 2018 Conference Paper

Reward learning from human preferences and demonstrations in Atari

  • Borja Ibarz
  • Jan Leike
  • Tobias Pohlen
  • Geoffrey Irving
  • Shane Legg
  • Dario Amodei

To solve complex real-world problems with reinforcement learning, we cannot rely on manually specified reward functions. Instead, we need humans to communicate an objective to the agent directly. In this work, we combine two approaches to this problem: learning from expert demonstrations and learning from trajectory preferences. We use both to train a deep neural network to model the reward function and use its predicted reward to train an DQN-based deep reinforcement learning agent on 9 Atari games. Our approach beats the imitation learning baseline in 7 games and achieves strictly superhuman performance on 2 games. Additionally, we investigate the fit of the reward model, present some reward hacking problems, and study the effects of noise in the human labels.

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