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- W2952204091 abstract "Although reinforcement learning has made great strides recently, a continuing limitation is that it requires an extremely high number of interactions with the environment. In this paper, we explore the effectiveness of reusing experience from the experience replay buffer in the Deep Q-Learning algorithm. We test the effectiveness of applying learning update steps multiple times per environmental step in the VizDoom environment and show first, this requires a change in the learning rate, and second that it does not improve the performance of the agent. Furthermore, we show that updating less frequently is effective up to a ratio of 4:1, after which performance degrades significantly. These results quantitatively confirm the widespread practice of performing learning updates every 4th environmental step." @default.
- W2952204091 created "2019-06-27" @default.
- W2952204091 creator A5005322233 @default.
- W2952204091 date "2019-06-24" @default.
- W2952204091 modified "2023-09-23" @default.
- W2952204091 title "Optimal Use of Experience in First Person Shooter Environments" @default.
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