Practical Issues in Temporal Difference Learning
Machine Learning
The Convergence of TD(λ) for General λ
Machine Learning
TD(λ) Converges with Probability 1
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Incremental multi-step Q-learning
Machine Learning - Special issue on reinforcement learning
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Technical Update: Least-Squares Temporal Difference Learning
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2001: Robot Soccer World Cup V
Team-Partitioned, Opaque-Transition Reinforced Learning
RoboCup-98: Robot Soccer World Cup II
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
A novel reinforcement learning architecture for continuous state and action spaces
Advances in Artificial Intelligence
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The main aim of this paper is to provide a comprehensive numerical analysis on the efficiency of various reinforcement learning (RL) techniques in an agent-based soccer game. The SoccerBots is employed as a simulation testbed to analyze the effectiveness of RL techniques under various scenarios. A hybrid agent teaming framework for investigating agent team architecture, learning abilities, and other specific behaviours is presented. Novel RL algorithms to verify the competitive and cooperative learning abilities of goal-oriented agents for decision-making are developed. In particular, the tile coding (TC) technique, a function approximation approach, is used to prevent the state space from growing exponentially, hence avoiding the curse of dimensionality. The underlying mechanism of eligibility traces is evaluated in terms of on-policy and off-policy procedures, as well as accumulating traces and replacing traces. The results obtained are analyzed, and implications of the results towards agent teaming and learning are discussed.