1 citations · 1 across the 1 of their papers we have counts for
7 papers · 1 filter
Dual Advantage Fields
Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin +5
Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representations provide value fields that…
Expert or not? assessing data quality in offline reinforcement learning
Arip Asadulaev, Fakhri Karray, Martin Takac
Offline reinforcement learning (RL) learns exclusively from static datasets, without further interaction with the environment. In practice, such datasets vary widely in quality, of…
Y-Shaped Generative Flows
Arip Asadulaev, Semyon Semenov, Abduragim Shtanchaev +3
Modern continuous-time generative models typically induce \emph{V-shaped} flows: each sample travels independently along a nearly straight trajectory from the prior to the data. Al…
Rethinking Optimal Transport in Offline Reinforcement Learning
Arip Asadulaev, Rostislav Korst, Alexander Korotin +3
We propose a novel algorithm for offline reinforcement learning using optimal transport. Typically, in offline reinforcement learning, the data is provided by various experts and s…
Stabilizing Transformer-Based Action Sequence Generation For Q-Learning
Gideon Stein, Andrey Filchenkov, Arip Asadulaev
Since the publication of the original Transformer architecture (Vaswani et al. 2017), Transformers revolutionized the field of Natural Language Processing. This, mainly due to thei…
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…