2k citations · 7.2k across the 38 of their papers we have counts for
Showing 2018 · cs.LGShow all
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cs.LG2018★ 24 cited
Universal Successor Features Approximators
Diana Borsa, André Barreto, John Quan +5
The ability of a reinforcement learning (RL) agent to learn about many reward functions at the same time has many potential benefits, such as the decomposition of complex tasks int…
cs.LG2018★ 35 cited
Unicorn: Continual Learning with a Universal, Off-policy Agent
Daniel J. Mankowitz, Augustin Žídek, André Barreto +7
Some real-world domains are best characterized as a single task, but for others this perspective is limiting. Instead, some tasks continually grow in complexity, in tandem with the…