<b>Fig. 3 | Performance analysis of microrobot navigation in various environments using reinforcement learning algorithms.</b>
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Reward trajectories for: <b>a.</b> Multi-output Tributary channel, <b>b.</b> Circuitous channel, <b>c.</b> Vascular channels with Dreamer V3 (blue) outperforming the hyperparameter-tuned state-of-the-art PPO (green) across simulation steps. <b>d.</b> Comparison of PPO and Dreamer algorithms in reaching targets across different channel types: Racetrack, tributary, SPA, Squares, Vascular, and Maze. <b>e.</b> Impact of different reward functions on the rate of target achievement. <b>f.</b> Effects of frame skipping on performance, presented in a logarithmic plot. <b>g.</b> Influence of training ratios on reward dynamics, highlighting consistent performance across various ratios in simulated environments.
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2025-05-06
