6 citations · 12 across the 6 of their papers we have counts for
4 papers · 1 filter
Statistical Guarantees for Offline Domain Randomization
Arnaud Fickinger, Abderrahim Bendahi, Stuart Russell
Reinforcement-learning (RL) agents often struggle when deployed from simulation to the real-world. A dominant strategy for reducing the sim-to-real gap is domain randomization (DR)…
Cross-Domain Imitation Learning via Optimal Transport
Arnaud Fickinger, Samuel Cohen, Stuart Russell +1
Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology. Comparing trajecto…
Explore and Control with Adversarial Surprise
Arnaud Fickinger, Natasha Jaques, Samyak Parajuli +5
Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering. However, a central challen…
Biadversarial Variational Autoencoder
Arnaud Fickinger
In the original version of the Variational Autoencoder, Kingma et al. assume Gaussian distributions for the approximate posterior during the inference and for the output during the…