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Found 5 papers, 1 papers with code
To the Noise and Back: Diffusion for Shared Autonomy
Traditional approaches to shared autonomy rely on knowledge of the environment dynamics, a discrete space of user goals that is known a priori, or knowledge of the user's policy -- assumptions that are unrealistic in many domains.
Invariance Through Latent Alignment
A robot's deployment environment often involves perceptual changes that differ from what it has experienced during training.
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks?
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation
Yet, these same techniques often falter when applied to the problem of recovering sparse recurrent networks.
The Importance of Sampling inMeta-Reinforcement Learning
Results are presented on a new environment we call `Krazy World': a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning.
