Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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Interactively shaping agents via human reinforcement: the TAMER framework
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Combining manual feedback with subsequent MDP reward signals for reinforcement learning
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Integrating reinforcement learning with human demonstrations of varying ability
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
JREP: Extending Repast Simphony for JADE Agent Behavior Components
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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In complex application systems, there are typically not only autonomous components which can be represented by agents, but humans may also play a role. The interaction between agents and humans can be learned to enhance the stability of a system. How can agents adopt strategies of humans to solve conflict situations? In this paper, we present a learning algorithm for agents based on interactions with humans in conflict situations. The learning algorithm consists of four phases: 1) agents detect a conflict situation, 2) a conversation takes place between a human and agents, 3) agents involved in a conflict situation evaluate the strategy applied by the human, and 4) agents which have interacted with humans apply the best rated strategy in a similar conflict situation. We have evaluated this learning algorithm using a Jade/Repast simulation framework. An evaluation study shows two benefits of the learning algorithm. First, through interaction with humans, agents can handle conflict situations, and thus, the system becomes more stable. Second, agents adopt the problem solving strategy which has been applied most frequently by humans.