2 citations · 3 across the 5 of their papers we have counts for
3 papers · 2 filters
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…