3.3k citations · 8.2k across the 53 of their papers we have counts for
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Human-centric Dialog Training via Offline Reinforcement Learning
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun +5
How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting…
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu
Off-policy reinforcement learning holds the promise of sample-efficient learning of decision-making policies by leveraging past experience. However, in the offline RL setting -- wh…
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo +2
Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that…
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
Archit Sharma, Michael Ahn, Sergey Levine +3
Reinforcement learning provides a general framework for learning robotic skills while minimizing engineering effort. However, most reinforcement learning algorithms assume that a w…
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Lisa Lee, Benjamin Eysenbach, Ruslan Salakhutdinov +2
Reinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks. However, in many settings, an agent must winnow down the inconceivably large space…