Multiagent Reinforcement Learning for Strategic Decision Making and Control in Robotic Soccer Through Self-Play

Learning to Play Soccer using Imitative Reinforcement

The reinforcement framework is a principled approach for agents learning to act in an environment. In the long run, reinforcement learning finds optimal policies. However, a physical agent, such as a humanoid robot, acting in the real world can perform only a limited number of trails, and consequently has only access to limited experience. With such limitations, the exhaustive exploration of hi...

Generation of Motion Policies Applying Multiagent Reinforcement Learning in Simulated Robotic Soccer

The CMUnited-97 Robotic Soccer Team: Perception and Multiagent Control

Robotic soccer is a challenging research domain which involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. In this paper, we describe CMUnited, the team of small robotic agents that we developed to enter the RoboCup-97 competition. We designed and built the robotic agents, devised the appropriate vision algorithm, and developed and impl...

Robust Decision Making under Strategic Uncertainty in Multiagent Environments

We introduce the notion of strategic uncertainty for boundedly rational, non-myopic agents as an analog to the equilibrium selection problem in classical game theory. We then motivate the need for and feasibility of addressing strategic uncertainty and present an algorithm that produces decisions that are robust to it. Finally, we show how agents’ rationality levels and planning horizons alter ...

Decision-making and strategic thinking through analogies

When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? Consider the chess game: how do humans avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess intuition and intelligence. We suggest that analogies a...