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Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
Tal Daniel, Aviv Tamar
The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main…
Online Safety Assurance for Deep Reinforcement Learning
Noga H. Rotman, Michael Schapira, Aviv Tamar
Recently, deep learning has been successfully applied to a variety of networking problems. A fundamental challenge is that when the operational environment for a learning-augmented…
Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores
Tzvika Geft, Aviv Tamar, Ken Goldberg +1
To compute robust 2D assembly plans, we present an approach that combines geometric planning with a deep neural network. We train the network using the Box2D physics simulator with…
Offline Meta Learning of Exploration
Ron Dorfman, Idan Shenfeld, Aviv Tamar
Consider the following instance of the Offline Meta Reinforcement Learning (OMRL) problem: given the complete training logs of conventional RL agents, trained on different…
Efficient MDP Analysis for Selfish-Mining in Blockchains
Roi Bar-Zur, Ittay Eyal, Aviv Tamar
A proof of work (PoW) blockchain protocol distributes rewards to its participants, called miners, according to their share of the total computational power. Sufficiently large mine…
Hallucinative Topological Memory for Zero-Shot Visual Planning
Kara Liu, Thanard Kurutach, Christine Tung +2
In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline, e.g., images obtained from self-supervised robot i…
Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning
Tom Jurgenson, Or Avner, Edward Groshev +1
Many AI problems, in robotics and other domains, are goal-based, essentially seeking trajectories leading to various goal states. Reinforcement learning (RL), building on Bellman's…
Deep Residual Flow for Out of Distribution Detection
Ev Zisselman, Aviv Tamar
The effective application of neural networks in the real-world relies on proficiently detecting out-of-distribution examples. Contemporary methods seek to model the distribution of…