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cs.AI2022
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…
cs.LG2022
The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning
Andrei Nica, Khimya Khetarpal, Doina Precup
Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to having many choices. Hierarchical reinfo…