2 citations · 2 across the 3 of their papers we have counts for
4 papers
Guided Exploration in Reinforcement Learning via Monte Carlo Critic Optimization
Igor Kuznetsov
The class of deep deterministic off-policy algorithms is effectively applied to solve challenging continuous control problems. Current approaches commonly utilize random noise as a…
Solving Continuous Control with Episodic Memory
Igor Kuznetsov, Andrey Filchenkov
Episodic memory lets reinforcement learning algorithms remember and exploit promising experience from the past to improve agent performance. Previous works on memory mechanisms sho…
Conditioning of Reinforcement Learning Agents and its Policy Regularization Application
Arip Asadulaev, Igor Kuznetsov, Gideon Stein +1
The outcome of Jacobian singular values regularization was studied for supervised learning problems. It also was shown that Jacobian conditioning regularization can help to avoid t…
Interpretable Few-Shot Learning via Linear Distillation
Arip Asadulaev, Igor Kuznetsov, Andrey Filchenkov
It is important to develop mathematically tractable models than can interpret knowledge extracted from the data and provide reasonable predictions. In this paper, we present a Line…