164 citations · 589 across the 14 of their papers we have counts for
4 papers · 1 filter
H-GAP: Humanoid Control with a Generalist Planner
Zhengyao Jiang, Yingchen Xu, Nolan Wagener +5
Humanoid control is an important research challenge offering avenues for integration into human-centric infrastructures and enabling physics-driven humanoid animations. The dauntin…
Deep Generative Models for Decision-Making and Control
Michael Janner
Deep model-based reinforcement learning methods offer a conceptually simple approach to the decision-making and control problem: use learning for the purpose of estimating an appro…
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner +2
Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions thr…
Training Diffusion Models with Reinforcement Learning
Kevin Black, Michael Janner, Yilun Du +2
Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use cases of diffusion models are not concer…