Decentralized Monte Carlo Tree Search for Partially Observable Multi-Agent Pathfinding
Authors: Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our approach outperforms state-of-the-art learnable MAPF solvers. Empirical Evaluation Experimental Setup To evaluate the efficiency of MATS-LP, we have conducted a set of experiments, comparing it with existing learnable approaches tailored to solve LMAPF problems. |
| Researcher Affiliation | Academia | Alexey Skrynnik1,2, Anton Andreychuk1, Konstantin Yakovlev2,1, Aleksandr Panov1,2 1AIRI, Moscow, Russia 2 Federal Research Center for Computer Science and Control of Russian Academy of Sciences, Moscow, Russia |
| Pseudocode | No | The paper describes the steps of the MCTS algorithm (Selection, Expansion, Backpropagation) in narrative text without presenting them in structured pseudocode or an algorithm block. |
| Open Source Code | Yes | The source code is available at https://github.com/AIRI-Institute/mats-lp. |
| Open Datasets | No | The paper uses custom-generated maps and instances, and while it references a generator from another work ([Damani et al. 2021]), it does not provide concrete access information (link, DOI, or formal citation for the *specific dataset instances* used) to make these datasets publicly available or open. |
| Dataset Splits | No | The paper describes the experimental setup and training parameters but does not explicitly provide information on training, validation, and test dataset splits (e.g., percentages, sample counts, or predefined split references). |
| Hardware Specification | Yes | In total, we conducted 100 algorithm runs, which roughly corresponds to 120 GPU hours using a single Titan RTX. |
| Software Dependencies | No | The paper mentions using an 'open-sourced asynchronous implementation of the PPO algorithm' (referencing a GitHub repository), but it does not specify version numbers for this or any other key software dependencies (e.g., Python, PyTorch, etc.). |
| Experiment Setup | Yes | The episode length was set to 512 in all the experiments. All the agents had the same parameters: their field-of-view was 11 11, all possible actions were considered only for the closest 3 agents, including the main agent, γ-value was set to 0.96, the number of expansions per iteration 250, coefficient c was set to 4.4. More details and the values of the rest parameters are given in the Hyperparameters section below. Table 1 presents the hyperparameters of COSTTRACER and MATS-LP approaches. |