How to Operationalize Reward for an Agent

Michael Cohen is is a DPhil student at the University of Oxford with Mike Osborne. He will be starting a postdoc with Professor Stuart Russell at UC Berkeley, with the Center for Human-Compatible AI. His research considers the expected behaviour of generally intelligent artificial agents, with a view to designing agents that we can expect to behave safely.

You can see more links and a full transcript at www.hearthisidea.com/episodes/cohen.

We discuss:

• What is reinforcement learning, and how is it different from supervised and unsupervised learning?
• Michael's recently co-authored paper titled 'Advanced artificial agents intervene in the provision of reward'
• Why might it be hard to convey what we really want to RL learners — even when we know exactly what we want?
• Why might advanced RL systems might tamper with their sources of input, and why could this be very bad?
• What assumptions need to hold for this "input tampering" outcome?
• Is reward really the optimisation target? Do models "get reward"?
• What's wrong with the analogy between RL systems and evolution?

Key links:

• Michael's personal website
• 'Advanced artificial agents intervene in the provision of reward' by Michael K. Cohen, Marcus Hutter, and Michael A. Osborne
• 'Pessimism About Unknown Unknowns Inspires Conservatism' by Michael Cohen and Marcus Hutter
• 'Intelligence and Unambitiousness Using Algorithmic Information Theory' by Michael Cohen, Badri Vallambi, and Marcus Hutter
• 'Quantilizers: A Safer Alternative to Maximizers for Limited Optimization' by Jessica Taylor
• 'RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning' by Marc Rigter, Bruno Lacerda, and Nick Hawes
• 'Quantilizers: A Safer Alternative to Maximizers for Limited Optimization' by Jessica Taylor
• Season 40 of Survivor