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Curiosity-driven exploration in reinforcement learning
Ist Teil von
2014 ELEKTRO, 2014, p.435-440
Ort / Verlag
IEEE
Erscheinungsjahr
2014
Quelle
IEEE Xplore
Beschreibungen/Notizen
The paper elaborates upon a prior proposal for a novelty detector based on an artificial neural network forecaster. In the former paper, the novelty-based motivational signal was used in place of more conventional techniques (such as the ε-greedy policy, or the softmax policy) to drive exploration, in the context of V-learning. The current paper provides a more comprehensive study of such handling of the exploration vs. exploitation trade-off. It also studies the various problems concerning application of the approach to SARSA, and Q-learning. Also, and with the same goal in mind, the paper presents several advances upon the original design.