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AAAI
2008
15 years 8 months ago
Potential-based Shaping in Model-based Reinforcement Learning
Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
John Asmuth, Michael L. Littman, Robert Zinkov
PRICAI
1999
Springer
15 years 10 months ago
Rationality of Reward Sharing in Multi-agent Reinforcement Learning
Abstract. In multi-agent reinforcement learning systems, it is important to share a reward among all agents. We focus on the Rationality Theorem of Profit Sharing [5] and analyze ...
Kazuteru Miyazaki, Shigenobu Kobayashi
CIA
2007
Springer
16 years 14 days ago
Agent Behavior Alignment: A Mechanism to Overcome Problems in Agent Interactions During Runtime
When two or more agents interacting, their behaviors are not necessarily matching. Automated ways to overcome conicts in the behavior of agents can make the execution of interacti...
Gerben G. Meyer, Nicolae B. Szirbik
ATAL
2005
Springer
15 years 11 months ago
Behavior transfer for value-function-based reinforcement learning
Temporal difference (TD) learning methods [22] have become popular reinforcement learning techniques in recent years. TD methods have had some experimental successes and have been...
Matthew E. Taylor, Peter Stone
ATAL
2008
Springer
15 years 8 months ago
Synthesis of strategies from interaction traces
We describe how to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and combine them into a composite strategy. We provide an...
Tsz-Chiu Au, Sarit Kraus, Dana S. Nau