2 citations · 3 across the 5 of their papers we have counts for
6 papers
Causal Object-Centric Models for Planning with Monte Carlo Tree Search
Rodion Vakhitov, Leonid Ugadiarov, Alexey Skrynnik +1
We introduce COMET (Causal Object-centric Model for Efficient Tree search), a model-based reinforcement learning algorithm that performs Monte Carlo Tree Search in a slot-structure…
Revisiting Tree Search for LLMs: Gumbel and Sequential Halving for Budget-Scalable Reasoning
Leonid Ugadiarov, Yuri Kuratov, Aleksandr Panov +1
Neural tree search is a powerful decision-making algorithm widely used in complex domains such as game playing and model-based reinforcement learning. Recent work has applied Alpha…
Object-Centric World Models Meet Monte Carlo Tree Search
Rodion Vakhitov, Leonid Ugadiarov, Aleksandr Panov
In this paper, we introduce ObjectZero, a novel reinforcement learning (RL) algorithm that leverages the power of object-level representations to model dynamic environments more ef…
Gradual Optimization Learning for Conformational Energy Minimization
Artem Tsypin, Leonid Ugadiarov, Kuzma Khrabrov +7
Molecular conformation optimization is crucial to computer-aided drug discovery and materials design. Traditional energy minimization techniques rely on iterative optimization meth…
Relational Object-Centric Actor-Critic
Leonid Ugadiarov, Vitaliy Vorobyov, Aleksandr I. Panov
The advances in unsupervised object-centric representation learning have significantly improved its application to downstream tasks. Recent works highlight that disentangled object…
Long-Term Exploration in Persistent MDPs
Leonid Ugadiarov, Alexey Skrynnik, Aleksandr I. Panov
Exploration is an essential part of reinforcement learning, which restricts the quality of learned policy. Hard-exploration environments are defined by huge state space and sparse…