7.8k citations · 9.6k across the 18 of their papers we have counts for
3 papers · 1 filter
Reward is enough for convex MDPs
Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins +1
Maximising a cumulative reward function that is Markov and stationary, i.e., defined over state-action pairs and independent of time, is sufficient to capture many kinds of goals i…
Behavior Priors for Efficient Reinforcement Learning
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh +8
As we deploy reinforcement learning agents to solve increasingly challenging problems, methods that allow us to inject prior knowledge about the structure of the world and effectiv…
Compress and Control
Joel Veness, Marc G. Bellemare, Marcus Hutter +2
This paper describes a new information-theoretic policy evaluation technique for reinforcement learning. This technique converts any compression or density model into a correspondi…