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“Gain-Based Separation” is a novel heuristic that modifies the standard multiclass decision tree learning algorithm to produce forests that can describe an example or object with multiple classifications. When the information gain at a node would be higher if all examples of a particular classification were removed, those examples are reserved for another tree. In this way, the algorithm performs...
Reinforcement learning has been commonly used in multi-robot decision making to cope with uncertainties in the environment. A shortcoming of this approach is the need for the robots to change their actions quite frequently, which is not feasible in a physical multi-robot system. This paper focuses on the development of a modified Q-learning algorithm with minimal switching of actions. By introducing...
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