382 citations · 916 across the 31 of their papers we have counts for
5 papers · 1 filter
POMRL: No-Regret Learning-to-Plan with Increasing Horizons
Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue +2
We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with lim…
Discovering Diverse Nearly Optimal Policies with Successor Features
Tom Zahavy, Brendan O'Donoghue, Andre Barreto +3
Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of d…
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…
Discovering a set of policies for the worst case reward
Tom Zahavy, Andre Barreto, Daniel J Mankowitz +4
We study the problem of how to construct a set of policies that can be composed together to solve a collection of reinforcement learning tasks. Each task is a different reward func…
The Uncertainty Bellman Equation and Exploration
Brendan O'Donoghue, Ian Osband, Remi Munos +1
We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the…