Search Results for author:
Found 18 papers, 9 papers with code
Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding
Our approach utilizes the agent's observations to recreate the intrinsic Markov decision process, which is then used for planning with a tailored for multi-agent tasks version of neural MCTS.
Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning
Multi-agent Pathfinding (MAPF) problem generally asks to find a set of conflict-free paths for a set of agents confined to a graph and is typically solved in a centralized fashion.
Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results
We investigate how to utilize Monte-Carlo Tree Search (MCTS) to solve the problem.
TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers
To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search.
Analysis Of The Anytime MAPF Solvers Based On The Combination Of Conflict-Based Search (CBS) and Focal Search (FS)
Moreover, anytime variant of CBS does exist that applies Focal Search (FS) to the high-level of CBS - Anytime BCBS.
Pathfinding in stochastic environments: learning vs planning
Within planning, an agent constantly re-plans and updates the path based on the history of the observations using a search-based planner.
POGEMA: Partially Observable Grid Environment for Multiple Agents
We introduce POGEMA (https://github. com/AIRI-Institute/pogema) a sandbox for challenging partially observable multi-agent pathfinding (PO-MAPF) problems .
Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles
Path finding is a well-studied problem in AI, which is often framed as graph search.
Improving Continuous-time Conflict Based Search
Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps.
Revisiting Bounded-Suboptimal Safe Interval Path Planning
Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles.
Multi-Agent Pathfinding with Continuous Time
Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide.
eLIAN: Enhanced Algorithm for Angle-constrained Path Finding
This problem is harder to solve than the one when shortest paths of any shape are sought, since the planer's search space is substantially bigger as multiple search nodes corresponding to the same location need to be considered.
Multi-robot Path Planning in Well-formed Infrastructures: Prioritized Planning vs. Prioritized Wait Adjustment (Preliminary Results)
We study the problem of planning collision-free paths for a group of homogeneous robots.
Path Finding for the Coalition of Co-operative Agents Acting in the Environment with Destructible Obstacles
The problem of planning a set of paths for the coalition of robots (agents) with different capabilities is considered in the paper.
Two Techniques That Enhance the Performance of Multi-robot Prioritized Path Planning
We introduce and empirically evaluate two techniques aimed at enhancing the performance of multi-robot prioritized path planning.
Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments
The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots).
Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm
This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m).
Resolving Spatial-Time Conflicts In A Set Of Any-angle Or Angle-constrained Grid Paths
We study the multi-agent path finding problem (MAPF) for a group of agents which are allowed to move into arbitrary directions on a 2D square grid.
