40 citations · 81 across the 10 of their papers we have counts for
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cs.LG2018★ 2 cited
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
Dilip Arumugam, David Abel, Kavosh Asadi +5
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is opti…
cs.LG2018★ 3 cited
Transfer with Model Features in Reinforcement Learning
Lucas Lehnert, Michael L. Littman
A key question in Reinforcement Learning is which representation an agent can learn to efficiently reuse knowledge between different tasks. Recently the Successor Representation wa…