Technical Note: \cal Q-Learning
Machine Learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Elements of machine learning
Steps toward artificial intelligence
Computers & thought
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Multi-agent reinforcement learning: independent vs. cooperative agents
Readings in agents
Learning policies for partially observable environments: scaling up
Readings in agents
Learning to coordinate without sharing information
Readings in agents
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Conflict Resolution-Based Decentralized Multi-Agent Problem Solving Model
MAAMAW '92 Selected papers from the 4th European Workshop on on Modelling Autonomous Agents in a Multi-Agent World, Artificial Social Systems
Dynamic Programming
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Multiagent reinforcement learning using function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A layered approach to learning coordination knowledge in multiagent environments
Applied Intelligence
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Individual learning in an environment where more than one agent exist is a challenging task. In this paper, a single learning agent situated in an environment where multiple agents exist is modeled based on reinforcement learning. The environment is non-stationary and partially accessible from an agents' point of view. Therefore, learning activities of an agent is influenced by actions of other cooperative or competitive agents in the environment. A prey-hunter capture game that has the above characteristics is defined and experimented to simulate the learning process of individual agents. Experimental results show that there are no strict rules for reinforcement learning. We suggest two new methods to improve the performance of agents. These methods decrease the number of states while keeping as much state as necessary.