As the thematic literature of machine learning suggests, reinforcement learning falls
between the two methods of supervised learning and unsupervised learning. In this method …

Multi-agent reinforcement learning (MARL) has achieved remarkable success in various
challenging problems. Meanwhile, more and more benchmarks have emerged and provided …

The rise of embodied AI has greatly improved the possibility of general mobile agent
systems. At present, many evaluation platforms with rich scenes, high visual fidelity, and …

The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams
of three small robots play against each other. Traditionally, a deterministic coach agent will …

Robot soccer as a complex mixed cooperative-competitive task presents many challenges to
multi-agent reinforcement learning (MARL), such as assigning long-term credits and …

A path planning algorithm that fits well in the context of IEEE Very Small Size Soccer league,
a mobile robotics competition where cubic differential robots play a soccer match …

In this work, we propose a Reinforcement Learning (RL) training methodology using
Proximal Policy Optimization (PPO) and Curriculum Learning (CL) to make simulated Very …

Robot soccer is regarded as a prime example of a dynamic and cooperative multi-agent
environment, as it can demonstrate a variety of complexities. Reinforcement learning is a …