Spartan Daily, October 12, 1982

Volume 79, Issue 31https://scholarworks.sjsu.edu/spartandaily/6943/thumbnail.jp

Spartan Daily, October 12, 1982

Volume 79, Issue 31https://scholarworks.sjsu.edu/spartandaily/6943/thumbnail.jp

Boosting Studies of Multi-Agent Reinforcement Learning on Google Research Football Environment: the Past, Present, and Future

Even though Google Research Football (GRF) was initially benchmarked and studied as a single-agent environment in its original paper, recent years have witnessed an increasing focus on its multi-agent nature by researchers utilizing it as a testbed for Multi-Agent Reinforcement Learning (MARL). However, the absence of standardized environment settings and unified evaluation metrics for multi-agent scenarios hampers the consistent understanding of various studies. Furthermore, the challenging 5-vs-5 and 11-vs-11 full-game scenarios have received limited thorough examination due to their substantial training complexities. To address these gaps, this paper extends the original environment by not only standardizing the environment settings and benchmarking cooperative learning algorithms across different scenarios, including the most challenging full-game scenarios, but also by discussing approaches to enhance football AI from diverse perspectives and introducing related research tools. Specifically, we provide a distributed and asynchronous population-based self-play framework with diverse pre-trained policies for faster training, two football-specific analytical tools for deeper investigation, and an online leaderboard for broader evaluation. The overall expectation of this work is to advance the study of Multi-Agent Reinforcement Learning on Google Research Football environment, with the ultimate goal of benefiting real-world sports beyond virtual games

Situation based strategic positioning for coordinating a team of homogeneous agents

. In this paper we are proposing an approach for coordinating a team ofhomogeneous agents based on a flexible common Team Strategy as well as onthe concepts of Situation Based Strategic Positioning and Dynamic Positioningand Role Exchange. We also introduce an Agent Architecture including a specifichigh-level decision module capable of implementing this strategy. Ourproposal is based on the formalization of what is a team strategy for competingwith an opponent team having opposite goals. A team strategy is composed of aset of agent types and a set of tactics, which are also composed of several formations.Formations are used for different situations and assign each agent a defaultspatial positioning and an agent type (defining its behaviour at several levels).Agents reactivity is also introduced for appropriate response to the dynamicsof the current situation. However, in our approach this is done in a way thatpreserves team coherence instead of permitting uncoordinated agent behaviour.We have applied, with success, this coordination approach to the RoboSoccersimulated domain. The FC Portugal team, developed using this approach wonthe RoboCup2000 (simulation league) European and World championshipsscoring a total of 180 goals and conceding none

Fine-Grained Retrieval of Sports Plays using Tree-Based Alignment of Trajectories

We propose a novel method for effective retrieval of multi-agent spatiotemporal tracking data. Retrieval of spatiotemporal tracking data offers several unique challenges compared to conventional text-based retrieval settings. Most notably, the data is fine-grained meaning that the specific location of agents is important in describing behavior. Additionally, the data often contains tracks of multiple agents (e.g., multiple players in a sports game), which generally leads to a permutational alignment problem when performing relevance estimation. Due to the frequent position swap of agents, it is difficult to maintain the correspondence of agents, and such issues make the pairwise comparison problematic for multi-agent spatiotemporal data. To address this issue, we propose a tree-based method to estimate the relevance between multi-agent spatiotemporal tracks. It uses a hierarchical structure to perform multi-agent data alignment and partitioning in a coarse-to-fine fashion. We validate our approach via user studies with domain experts. Our results show that our method boosts performance in retrieving similar sports plays -- especially in interactive situations where the user selects a subset of trajectories compared to current state-of-the-art methods