Fig. 9: Analysis of exploration strategies in model-based RL for the 5-dimensional Ackley function. | npj Computational Materials

Fig. 9: Analysis of exploration strategies in model-based RL for the 5-dimensional Ackley function.

From: Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning

Fig. 9

a Impact of different epsilon values (ε = 0.05–0.9) during the design stage, where higher ε indicates more random exploration. b Effect of batch size (1, 2, 4, 8, 16) on optimization performance, investigating the trade-off between parallel experimentation and learning efficiency. In both plots, the y-axis shows the best-so-far values (lower is better) over the number of conducted experiments, with the global optimum indicated by the dashed line.

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