2 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Optimistic Gradient Learning with Hessian Corrections for High-Dimensional Black-Box Optimization
Yedidya Kfir, Elad Sarafian, Sarit Kraus +1
Black-box algorithms are designed to optimize functions without relying on their underlying analytical structure or gradient information, making them essential when gradients are i…
A Coupled Flow Approach to Imitation Learning
Gideon Freund, Elad Sarafian, Sarit Kraus
In reinforcement learning and imitation learning, an object of central importance is the state distribution induced by the policy. It plays a crucial role in the policy gradient th…
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
Shai Keynan, Elad Sarafian, Sarit Kraus
The Reinforcement Learning (RL) building blocks, i.e. Q-functions and policy networks, usually take elements from the cartesian product of two domains as input. In particular, the…
Explicit Gradient Learning
Mor Sinay, Elad Sarafian, Yoram Louzoun +2
Black-Box Optimization (BBO) methods can find optimal policies for systems that interact with complex environments with no analytical representation. As such, they are of interest…
Constrained Policy Improvement for Safe and Efficient Reinforcement Learning
Elad Sarafian, Aviv Tamar, Sarit Kraus
We propose a policy improvement algorithm for Reinforcement Learning (RL) which is called Rerouted Behavior Improvement (RBI). RBI is designed to take into account the evaluation e…