TD(λ) Converges with Probability 1
Machine Learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2001: Robot Soccer World Cup V
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Dynamic Programming
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Reinforcement learning of competitive skills with soccer agents
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Teamwork and simulation in hybrid cognitive architecture
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Function approximation via tile coding: automating parameter choice
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
A Role-Based Framework for Multi-agent Teaming
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Experimental analysis of eligibility traces strategies in temporal difference learning
International Journal of Knowledge Engineering and Soft Data Paradigms
A role-oriented BDI framework for real-time multiagent teaming
Intelligent Decision Technologies
Experimental analysis on Sarsa(λ) and Q(λ) with different eligibility traces strategies
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Theoretical advances of intelligent paradigms
Convergence analysis on approximate reinforcement learning
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Temporal difference learning and simulated annealing for optimal control: a case study
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
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Multi-Agent Systems extend research in Artificial Intelligence and agent systems by incorporating cooperative learning and agent teaming architectures. Agent teaming is a key research area of multi-agent systems that is mainly composed of artificial intelligence and distributed computing techniques. The reasoning and learning ability of agents in uncertain environments via communication and collaboration (in both competitive and cooperative situations) is another key feature for autonomous agents. Many theoretical and applied techniques have been applied to the investigation of autonomous agents with respect to coordination, cooperation, and learning abilities. Due to the inherent complexity of real-time, stochastic, and dynamic environments, it is often extremely complex and difficult to formally verify their properties a priori. In addition, it is quite difficult to generate enough episodes via real applications for training the goal-oriented agent's individual and cooperative learning abilities. In most cases, such abilities can be obtained via computer simulation, rather than directly from real applications. In doing so, a simulation testbed is applied to test the learning algorithms in the specified scenarios. The objective of this paper is to improve the convergence and efficiency of reinforcement learning algorithms for large, continuous state-action spaces, by finding the optimal values of the parameters for those algorithms. In this paper, the game of soccer is adopted as the simulation environment in conjunction with optimisation techniques to verify goal-oriented agents' competitive and cooperative learning abilities for decision making. We use the Sarsa learning algorithm with a linear function approximation technique known as tile coding to avoid the state space growing exponentially. The convergence and efficiency of Sarsa algorithm are investigated through simulating a soccer game.