471 citations · 611 across the 33 of their papers we have counts for
6 papers · 1 filter
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Yue Wu, Yewen Fan, Paul Pu Liang +3
High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructu…
Meta-Reinforcement Learning Using Model Parameters
Gabriel Hartmann, Amos Azaria
In meta-reinforcement learning, an agent is trained in multiple different environments and attempts to learn a meta-policy that can efficiently adapt to a new environment. This pap…
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning
Yitzhak Spielberg, Amos Azaria
In the context of reinforcement learning we introduce the concept of criticality of a state, which indicates the extent to which the choice of action in that particular state influ…
Learning to Conceal: A Deep Learning Based Method for Preserving Privacy and Avoiding Prejudice
Moshe Hanukoglu, Nissan Goldberg, Aviv Rovshitz +1
In this paper, we introduce a learning model able to conceals personal information (e.g. gender, age, ethnicity, etc.) from an image, while maintaining any additional information p…
The Concept of Criticality in Reinforcement Learning
Yitzhak Spielberg, Amos Azaria
Reinforcement learning methods carry a well known bias-variance trade-off in n-step algorithms for optimal control. Unfortunately, this has rarely been addressed in current researc…
Goldbach's Function Approximation Using Deep Learning
Avigail Stekel, Merav Chkroun, Amos Azaria
Goldbach conjecture is one of the most famous open mathematical problems. It states that every even number, bigger than two, can be presented as a sum of 2 prime numbers. % In this…