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cs.LG2024
Imitating Language via Scalable Inverse Reinforcement Learning
Markus Wulfmeier, Michael Bloesch, Nino Vieillard +13
The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning…
cs.LG2019
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
Samuel Ainsworth, Matt Barnes, Siddhartha Srinivasa
In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task. We develop a simple technique using emergency sto…
cs.LG2019
Imitation Learning as -Divergence Minimization
Liyiming Ke, Sanjiban Choudhury, Matt Barnes +3
We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any…