# Position Control of a Mobile Robot through Deep Reinforcement Learning
## 7 Conclusions  
This article presents the use of reinforcement learning algorithms and two control algorithms to control the position of the mobile robot Kephera IV in a simulated envi  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **TP 1 s** & **TP 2 s** & **TP 3 s** & **Mean Time (s)** \\ \hline Villela & 30.45 & 20.80 & **18.25** & 23.17 \\ IPC & 25.85 & 20.70 & **18.25** & 21.60 \\ DQN & **22.50** & 20.65 & 18.35 & 20.50 \\ DDPG & 22.60 & **20.55** & 18.30 & **20.48** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Arrival time for the three target points for the experiment without obstacles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Villela** & **IPC** & **DQN** & **DDPG** \\ \hline ISE & 14.52 & 14.44 & 11.43 & **11.30** \\ IAE & 11.68 & 90.5 & 8.08 & **7.95** \\ ITSE & 132.47 & 99.72 & 83.67 & **82.27** \\ ITAE & 89.16 & 58.29 & 45.86 & **44.61** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average performance indices (three target points) in the experiment without obstacles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **TP 1 s** & **TP 2 s** & **TP 3 s** & **Mean Time (s)** \\ \hline Villela & 30.45 & 20.80 & **18.25** & 23.17 \\ IPC & 25.85 & 20.70 & **18.25** & 21.60 \\ DQN & **22.50** & 20.65 & 18.35 & 20.50 \\ DDPG & 22.60 & **20.55** & 18.30 & **20.48** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Arrival time for the three target points for the experiment with obstacles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Villela** & **IPC** & **DQN** & **DDPG** \\ \hline ISE & 14.52 & 14.44 & 11.43 & **11.30** \\ IAE & 11.68 & 90.5 & 8.08 & **7.95** \\ ITSE & 132.47 & 99.72 & 83.67 & **82.27** \\ ITAE & 89.16 & 58.29 & 45.86 & **44.61** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average performance indices (three target points) in the experiment with obstacles.  
ronment. The work shows the effectiveness of RL algorithms in comparison with control algorithms for the task of position control in environments with and without obstacles.  
The use of RL agents has a substantial advantage in controlling the position of robots because they learn from experience and can improve decision making compared to control algorithms. The results obtained show how the DDPG and DQN agents manage to obtain the shortest times taken to reach the target point in environments with and without obstacles; however, the time required to learn all the robot interactions is high compared to that of the control algorithms.  
In future work, we will consider two important tasks: first, to make a comparison with other types of algorithms, such as adaptive neural networks [53], PPO (proximal policy optimization) [54], Muesli [55], neuroevolution [56] and adaptive NN dynamic surface controllers [57; 58]; second, to perform the transfer of learning from the simulation to the real world [18; 19; 20], which will allow us to validate our theoretical and simulated results in real environments, analyze whether reinforced learning agents can operate with more complex tasks, corroborate decision making in the face of different obstacles (more complex shapes and layouts) and analyze the reaction times used to start the motors of the robot, among other things.  
Investigation, F.Q.; Methodology, G.H.; Supervision, E.F.; Validation, G.F.; Visualization, G.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by FONDECYT under Grant 1191188.  
Not applicable.  
The authors declare no conflict of interest.