177 citations · 241 across the 53 of their papers we have counts for
7 papers · 1 filter
Closed-Loop Vision-Language Planning for Multi-Agent Coordination
Zhiyuan Li, Wenshuai Zhao, Joni Pajarinen
Cooperative multi-agent reinforcement learning (MARL) struggles with sample efficiency, interpretability, and generalization. While Large Language Models (LLMs) offer powerful plan…
Hybrid Search for Efficient Planning with Completeness Guarantees
Kalle Kujanpää, Joni Pajarinen, Alexander Ilin
Solving complex planning problems has been a long-standing challenge in computer science. Learning-based subgoal search methods have shown promise in tackling these problems, but t…
Monte-Carlo tree search with uncertainty propagation via optimal transport
Tuan Dam, Pascal Stenger, Lukas Schneider +3
This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) designed for highly stochastic and partially observable Markov decision processes. We adopt a proba…
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search
Tuan Dam, Carlo D'Eramo, Jan Peters +1
Monte-Carlo Tree Search (MCTS) is a class of methods for solving complex decision-making problems through the synergy of Monte-Carlo planning and Reinforcement Learning (RL). The h…
Technical Report: The Policy Graph Improvement Algorithm
Joni Pajarinen
Optimizing a partially observable Markov decision process (POMDP) policy is challenging. The policy graph improvement (PGI) algorithm for POMDPs represents the policy as a fixed si…
Generalized Mean Estimation in Monte-Carlo Tree Search
Tuan Dam, Pascal Klink, Carlo D'Eramo +2
We consider Monte-Carlo Tree Search (MCTS) applied to Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), and the well-known Upper Confidence bound for Trees (…