6 citations · 15 across the 8 of their papers we have counts for
8 papers
Towards Scalable Persistence-Based Topological Optimization
Abderrahim Bendahi, Alexandre Duplessis, Arnaud Fickinger
Persistence-based topological optimization deforms a point cloud by minimizing objectives of the form , where $\mathrm{Dgm}(X…
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…
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Arnaud Fickinger, Hengyuan Hu, Brandon Amos +2
Lookahead search has been a critical component of recent AI successes, such as in the games of chess, go, and poker. However, the search methods used in these games, and in many ot…
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…
Multi-Principal Assistance Games: Definition and Collegial Mechanisms
Arnaud Fickinger, Simon Zhuang, Andrew Critch +2
We introduce the concept of a multi-principal assistance game (MPAG), and circumvent an obstacle in social choice theory, Gibbard's theorem, by using a sufficiently collegial prefe…
Multi-Principal Assistance Games
Arnaud Fickinger, Simon Zhuang, Dylan Hadfield-Menell +1
Assistance games (also known as cooperative inverse reinforcement learning games) have been proposed as a model for beneficial AI, wherein a robotic agent must act on behalf of a h…
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…