# NRNH-AR: A Small Robotic Agent Using Tri-Fold Learning for Navigation and Obstacle Avoidance
## 4 Results  
We compared the performance of each learning phase in terms of \"Experience Buffer Size\", \"Computational Cost\", \"Learning Oscillation\", and \"Reaction Delay\", whose concepts will be explained later. Table 4 provides the comparison of results among the three learning phases.  
The measurement methods of each one of the four concepts in Table 4, and how the values obtained were analyzed, are explained as follows.  
* Experience Buffer Size was the amount of experience generated during each learning phase. In the NRNH-AR algorithm, for each time step after an action is selected, the experience performed is added into the experience buffer memory. This number is equivalent to the time steps required for the convergence of each learning phase.
* Computational Cost was measured as the average occupation percentage of the GPU of the Jetson Nano during each learning phase. For example, in the case of SSL, the average occupation is, on average, 10% of the full capacity of the Jetson Nano's GPU. In contrast, in the DRL phase, on average, 90% of the total capacity must be used to solve the problem.
* Learning Oscillation was calculated as the standard deviation from the average learning level \(A\) in each phase. As expected, in the SSL phase, the robotic agent randomly selects an action to gain environmental information as quickly as possible, so the oscillation of the learning level is larger compared with the following two learning phases. The robotic agent acquires experience and evolves by updating the connection weights  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PHASE** & **Experience Buffer Size** & **Computational Cost** & **Learning Oscillation** & **Reaction Delay** \\ \hline SSL & 50 & 10\% & 0.4127 & 0.1 s \\ USL & 640 & 60\% & 0.0939 & 0.1 s \\ DRL & 850 & 90\% & 0.0034 & 0.1 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of four different issues in three learning phases.  
Figure 10: Dynamic environment. (**a**) Starting position, (**b**) “forward” action is selected, (**c**) “turn right” action is selected and environment is modified, (**d**) environment is modified and “turn right” action is selected, (**e**) “forward” action is selected, (**f**) “turn left” action is selected and environment is modified. For a better understanding of the detailed movement of the agent, please see [[https://www.youtube.com/watch?v=SUtYX2](https://www.youtube.com/watch?v=SUtYX2) Ee1 DU]([https://www.youtube.com/watch?v=SUtYX2](https://www.youtube.com/watch?v=SUtYX2) Ee1 DU) accessed on 18 March 2022.  
of the predicted actor network, learning is established, and oscillation is reduced. As mentioned before, the connection weights of the predicted actor network are updated to maximize the quality of the action performed.
* Reaction Delay was defined as the time elapsed from the robotic agent taking three images to performing a corresponding action. This value is essential in real applications because the total elapsed time from the start to detecting the target object is proportional to this value. From Table 4, the reaction delay is only 0.1 s in all learning phases, indicating a possible real-world application.