Transcription of Dueling Network Architectures for Deep Reinforcement …
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Dueling Network Architectures for Deep Reinforcement …
Dueling Network Architectures for Deep Reinforcement LearningZiyu van de DeepMind, London, UKAbstractIn recent years there have been many successesof using deep representations in reinforcementlearning. Still, many of these applications useconventional Architectures , such as convolutionalnetworks, LSTMs, or auto-encoders. In this pa-per, we present a new neural Network architec-ture for model-free Reinforcement learning . Ourdueling Network represents two separate estima-tors: one for the state value function and one forthe state-dependent action advantage main benefit of this factoring is to general-ize learning across actions without imposing anychange to the underlying Reinforcement learningalgorithm. Our results show that this architec-ture leads to better policy evaluation in the pres-ence of many similar-valued actions. Moreover,the Dueling architecture enables our RL agent tooutperform the state-of-the-art on the Atari IntroductionOver the past years, deep learning has contributed to dra-matic advances in scalability and performance of machinelearning (LeCun et al.)
ture for model-free reinforcement learning. Our dueling network represents two separate estima-tors: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to general-ize learning across actions without imposing any change to the underlying reinforcement learning algorithm.
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