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2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES), 2015, p.119-123
Ort / Verlag
IEEE
Erscheinungsjahr
2015
Quelle
IEEE Electronic Library Online
Beschreibungen/Notizen
The paper presents an approach that uses optimistic initialization and scalarized multi-objective learning to facilitate exploration in the context of model-free reinforcement learning. In contrast to existing optimistic intialization approaches, the approach introduces an extra value function, which is initialized optimistically and then updated using a zero reward function. Linear or Chebyshev scalarization is then used to compound this function with the standard task-related value function, thus forming an exploration policy. The paper concludes with evaluation of the approach on a benchmark task.