Soft Actor-Critic for Navigation of Mobile Robots

This paper provides a study of two deep reinforcement learning techniques for application in navigation of mobile robots, one of the techniques is the Soft Actor Critic (SAC) that is compared with the Deep Deterministic Policy Gradients (DDPG) algorithm in the same situation. In order to make a robo...

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Vydáno v:	Journal of intelligent & robotic systems Ročník 102; číslo 2; s. 31
Hlavní autoři:	de Jesus, Junior Costa, Kich, Victor Augusto, Kolling, Alisson Henrique, Grando, Ricardo Bedin, Cuadros, Marco Antonio de Souza Leite, Gamarra, Daniel Fernando Tello
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.06.2021 Springer Springer Nature B.V
Témata:	Algorithms Angular velocity Artificial Intelligence Comparative analysis Control Electrical Engineering Employee motivation Engineering Mechanical Engineering Mechatronics Navigation Regular Paper Robotics Robots Topical collection on ICAR 2019 Special Issue Deep reinforcement learning Soft actor-critic Deep deterministic policy gradients Navigation for mobile robots
ISSN:	0921-0296, 1573-0409
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Abstract	This paper provides a study of two deep reinforcement learning techniques for application in navigation of mobile robots, one of the techniques is the Soft Actor Critic (SAC) that is compared with the Deep Deterministic Policy Gradients (DDPG) algorithm in the same situation. In order to make a robot to arrive at a target in an environment, both networks have 10 laser range findings, the previous linear and angular velocity, and relative position and angle of the mobile robot to the target are used as the network inputs. As outputs, the networks have the linear and angular velocity of the mobile robot. The reward function created was designed in a way to only give a positive reward to the agent when it gets to the target and a negative reward when colliding with any object. The proposed architecture was applied successfully in two simulated environments, and a comparison between the two referred techniques was made using the results obtained as a basis and it was demonstrated that the SAC algorithm has a superior performance for the navigation of mobile robots than the DDPG algorithm (Code available at https://github.com/dranaju/project ).
AbstractList	This paper provides a study of two deep reinforcement learning techniques for application in navigation of mobile robots, one of the techniques is the Soft Actor Critic (SAC) that is compared with the Deep Deterministic Policy Gradients (DDPG) algorithm in the same situation. In order to make a robot to arrive at a target in an environment, both networks have 10 laser range findings, the previous linear and angular velocity, and relative position and angle of the mobile robot to the target are used as the network inputs. As outputs, the networks have the linear and angular velocity of the mobile robot. The reward function created was designed in a way to only give a positive reward to the agent when it gets to the target and a negative reward when colliding with any object. The proposed architecture was applied successfully in two simulated environments, and a comparison between the two referred techniques was made using the results obtained as a basis and it was demonstrated that the SAC algorithm has a superior performance for the navigation of mobile robots than the DDPG algorithm (Code available at https://github.com/dranaju/project ). This paper provides a study of two deep reinforcement learning techniques for application in navigation of mobile robots, one of the techniques is the Soft Actor Critic (SAC) that is compared with the Deep Deterministic Policy Gradients (DDPG) algorithm in the same situation. In order to make a robot to arrive at a target in an environment, both networks have 10 laser range findings, the previous linear and angular velocity, and relative position and angle of the mobile robot to the target are used as the network inputs. As outputs, the networks have the linear and angular velocity of the mobile robot. The reward function created was designed in a way to only give a positive reward to the agent when it gets to the target and a negative reward when colliding with any object. The proposed architecture was applied successfully in two simulated environments, and a comparison between the two referred techniques was made using the results obtained as a basis and it was demonstrated that the SAC algorithm has a superior performance for the navigation of mobile robots than the DDPG algorithm (Code available at https://github.com/dranaju/project). This paper provides a study of two deep reinforcement learning techniques for application in navigation of mobile robots, one of the techniques is the Soft Actor Critic (SAC) that is compared with the Deep Deterministic Policy Gradients (DDPG) algorithm in the same situation. In order to make a robot to arrive at a target in an environment, both networks have 10 laser range findings, the previous linear and angular velocity, and relative position and angle of the mobile robot to the target are used as the network inputs. As outputs, the networks have the linear and angular velocity of the mobile robot. The reward function created was designed in a way to only give a positive reward to the agent when it gets to the target and a negative reward when colliding with any object. The proposed architecture was applied successfully in two simulated environments, and a comparison between the two referred techniques was made using the results obtained as a basis and it was demonstrated that the SAC algorithm has a superior performance for the navigation of mobile robots than the DDPG algorithm (Code available at Keywords Soft actor-critic * Deep deterministic policy gradients * Deep reinforcement learning * Navigation for mobile robots
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Author	de Jesus, Junior Costa Grando, Ricardo Bedin Kich, Victor Augusto Kolling, Alisson Henrique Gamarra, Daniel Fernando Tello Cuadros, Marco Antonio de Souza Leite
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Cites_doi	10.1137/0330046 10.1186/s40638-016-0055-x 10.1609/aaai.v30i1.10295 10.1109/IROS.2017.8202134 10.1561/2200000071 10.1109/ICRA.2017.7989385 10.1109/LARS/SBR/WRE51543.2020.9307003 10.1177/0278364913495721 10.1109/ICAR46387.2019.8981638 10.1109/IROS.2017.8202312 10.1007/978-3-030-16660-1_21 10.1109/HPCA.2019.00048 10.18653/v1/P19-1285 10.1038/nature14236 10.18201/ijisae.08103 10.1109/ICRA.2017.7989381 10.1103/PhysRev.36.823 10.1109/ISAMSR.2015.7379780 10.4114/intartif.vol22iss63pp121-134
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References	Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D.: Continuous control with deep reinforcement learning. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. [Online]. Available: arXiv:1509.02971 (2016) KoberJBagnellJAPetersJReinforcement learning in robotics: a surveyInt. J. Robot. Res.201332111238127410.1177/0278364913495721 Rao, D., McMahan, B.: Natural Language Processing with Pytorch: Build Intelligent Language Applications Using Deep Learning. O’Reilly Media, Inc (2019) Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., Farhadi, A.: Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp 3357–3364. IEEE (2017) Wu, C.-J., Brooks, D., Chen, K., Chen, D., Choudhury, S., Dukhan, M., Hazelwood, K., Isaac, E., Jia, Y., Jia, B., et al.: Machine learning at facebook: understanding inference at the edge. In: 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp 331–344. IEEE (2019) PajazitiAAvdullahuPSlam–map building and navigation via rosInt. J. Intell. Syst. Appl. Eng.201424717510.18201/ijisae.08103 Gu, S., Lillicrap, T., Sutskever, I., Levine, S.: Continuous deep q-learning with model-based acceleration. In: International Conference on Machine Learning, pp 2829–2838 (2016) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor, [Online]. Available: arXiv:1801.01290 (2018) Fairchild, C., Harman, T.L.: ROS Robotics by Example. Packt Publishing Ltd (2016) de Assis Brasil, P.M., Pereira, F.U., de Souza Leite Cuadros, M.A., Cukla, A.R., Tello Gamarra, D.F.: A study on global path planners algorithms for the simulated turtlebot 3 robot in ros. In: 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), pp 1–6 (2020) Mahmood, A.R., Korenkevych, D., Vasan, G., Ma, W., Bergstra, J.: Benchmarking reinforcement learning algorithms on real-world robots, CoRR, vol. abs/1809.07731. [Online]. Available: arXiv:1809.07731 (2018) Gu, S., Holly, E., Lillicrap, T., Levine, S.: Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp 3389–3396. IEEE (2017) MnihVKavukcuogluKSilverDRusuAAVenessJBellemareMGGravesARiedmillerMFidjelandAKOstrovskiGHuman-level control through deep reinforcement learningNature2015518754052953310.1038/nature14236 Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.A.: Playing atari with deep reinforcement learning, [Online]. Available: arXiv:1312.5602 (2013) Van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. In: Thirtieth AAAI Conference on Artificial Intelligence (2016) Schaul, T., Quan, J., Antonoglou, I., Silver, D.: Prioritized experience replay. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. [Online]. Available: arXiv:1511.05952 (2016) Chen, Y.F., Everett, M., Liu, M., How, J.P.: Socially aware motion planning with deep reinforcement learning. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 1343–1350. IEEE (2017) Dai, Z., Yang, Z., Yang, Y., Carbonell, J.G., Le, Q.V., Salakhutdinov, R.: Transformer-xl: attentive language models beyond a fixed-length context, [Online]. Available: arXiv:1901.02860 (2019) Plappert, M., Houthooft, R., Dhariwal, P., Sidor, S., Chen, R.Y., Chen, X., Asfour, T., Abbeel, P., Andrychowicz, M.: Parameter space noise for exploration, [Online]. Available: arXiv:1706.01905 (2017) Tai, L., Liu, M.: Towards cognitive exploration through deep reinforcement learning for mobile robots, CoRR, vol. abs/1610.01733. [Online]. Available: arXiv:1610.01733 (2016) PfitscherMWelferDdo NascimentoEJCuadrosMAdSLGamarraDFTArticle users activity gesture recognition on kinect sensor using convolutional neural networks and fastdtw for controlling movements of a mobile robotIntel. Artif.2019226312113410.4114/intartif.vol22iss63pp121-134 Joseph, L.: Mastering ROS for robotics programming. Packt Publishing Ltd (2015) Jesus, J.C., Bottega, J.A., Cuadros, M.A., Gamarra, D.F.: Deep deterministic policy gradient for navigation of mobile robots in simulated environments. In: 2019 19th International Conference on Advanced Robotics (ICAR), pp 362–367. IEEE (2019) François-LavetVHendersonPIslamRBellemareMGPineauJAn introduction to deep reinforcement learningFound. Trends Mach. Learn.2018113-421935410.1561/2200000071[Online]. Available: http://dx.doi.org/10.1561/2200000071 Schulman, J., Moritz, P., Levine, S., Jordan, M.I., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. [Online]. Available: arXiv:1506.02438 (2016) UhlenbeckGEOrnsteinLSOn the theory of the brownian motionPhys. Rev.193036582310.1103/PhysRev.36.823 PolyakBTJuditskyABAcceleration of stochastic approximation by averagingSIAM J. Control Optim.1992304838855116781410.1137/0330046 Subramanian, V.: Deep learning with pytorch: a practical approach to building neural network models using PyTorch Packt. Publishing Ltd (2018) Laskin, M., Srinivas, A., Abbeel, P.: Curl: contrastive unsupervised representations for reinforcement learning. In: International Conference on Machine Learning, pp 5639–5650. PMLR (2020) da Silva, R.M., de Souza Leite Cuadros, M.A., Gamarra, D.F.T.: Comparison of a backstepping and a fuzzy controller for tracking a trajectory with a mobile robot. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds.) Intelligent Systems Design and Applications, pp 212–221. Cham, Springer International Publishing (2020) PyoYChoHJungRLimTRos Robot Programming2015ROBOTIS CoSeoul Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., Levine, S.: Soft actor-critic algorithms and applications, [Online]. Available: arXiv:1812.05905 (2018) Tai, L., Paolo, G., Liu, M.: Virtual-to-real deep reinforcement learning: continuous control of mobile robots for mapless navigation. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 31–36. IEEE (2017) Hausknecht, M., Stone, P.: Deep recurrent q-learning for partially observable mdps. In: 2015 AAAI Fall Symposium Series (2015) SuttonRSBartoAGReinforcement Learning: an Introduction2018CambridgeMIT Press1407.68009 Zhelo, O., Zhang, J., Tai, L., Liu, M., Burgard, W.: Curiosity-driven exploration for mapless navigation with deep reinforcement learning, [Online]. Available: arXiv:1804.00456 (2018) Omara, H.I.M.A., Sahari, K.S.M.: Indoor mapping using kinect and ros. In: 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR), pp 110–116. IEEE (2015) Achiam, J.: Spinning up in deep reinforcement learning, GitHub repository. [Online]. Available: https://github.com/openai/spinningup (2018) 1367_CR18 1367_CR19 1367_CR16 1367_CR17 V Mnih (1367_CR22) 2015; 518 1367_CR10 1367_CR32 1367_CR11 1367_CR33 1367_CR30 1367_CR31 1367_CR14 1367_CR36 1367_CR37 1367_CR1 1367_CR12 1367_CR2 1367_CR35 1367_CR3 1367_CR4 1367_CR5 1367_CR6 1367_CR7 M Pfitscher (1367_CR27) 2019; 22 1367_CR8 1367_CR9 J Kober (1367_CR20) 2013; 32 GE Uhlenbeck (1367_CR23) 1930; 36 1367_CR29 RS Sutton (1367_CR38) 2018 1367_CR28 1367_CR21 Y Pyo (1367_CR34) 2015 V François-Lavet (1367_CR15) 2018; 11 1367_CR25 BT Polyak (1367_CR26) 1992; 30 1367_CR24 A Pajaziti (1367_CR13) 2014; 2
References_xml	– reference: MnihVKavukcuogluKSilverDRusuAAVenessJBellemareMGGravesARiedmillerMFidjelandAKOstrovskiGHuman-level control through deep reinforcement learningNature2015518754052953310.1038/nature14236 – reference: Wu, C.-J., Brooks, D., Chen, K., Chen, D., Choudhury, S., Dukhan, M., Hazelwood, K., Isaac, E., Jia, Y., Jia, B., et al.: Machine learning at facebook: understanding inference at the edge. In: 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp 331–344. IEEE (2019) – reference: Rao, D., McMahan, B.: Natural Language Processing with Pytorch: Build Intelligent Language Applications Using Deep Learning. O’Reilly Media, Inc (2019) – reference: François-LavetVHendersonPIslamRBellemareMGPineauJAn introduction to deep reinforcement learningFound. Trends Mach. Learn.2018113-421935410.1561/2200000071[Online]. Available: http://dx.doi.org/10.1561/2200000071 – reference: Tai, L., Paolo, G., Liu, M.: Virtual-to-real deep reinforcement learning: continuous control of mobile robots for mapless navigation. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 31–36. IEEE (2017) – reference: Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor, [Online]. Available: arXiv:1801.01290 (2018) – reference: Plappert, M., Houthooft, R., Dhariwal, P., Sidor, S., Chen, R.Y., Chen, X., Asfour, T., Abbeel, P., Andrychowicz, M.: Parameter space noise for exploration, [Online]. Available: arXiv:1706.01905 (2017) – reference: Schaul, T., Quan, J., Antonoglou, I., Silver, D.: Prioritized experience replay. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. [Online]. Available: arXiv:1511.05952 (2016) – reference: Joseph, L.: Mastering ROS for robotics programming. Packt Publishing Ltd (2015) – reference: Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., Farhadi, A.: Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp 3357–3364. IEEE (2017) – reference: Van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double q-learning. In: Thirtieth AAAI Conference on Artificial Intelligence (2016) – reference: Hausknecht, M., Stone, P.: Deep recurrent q-learning for partially observable mdps. In: 2015 AAAI Fall Symposium Series (2015) – reference: PolyakBTJuditskyABAcceleration of stochastic approximation by averagingSIAM J. Control Optim.1992304838855116781410.1137/0330046 – reference: PajazitiAAvdullahuPSlam–map building and navigation via rosInt. J. Intell. Syst. Appl. Eng.201424717510.18201/ijisae.08103 – reference: Fairchild, C., Harman, T.L.: ROS Robotics by Example. Packt Publishing Ltd (2016) – reference: Mahmood, A.R., Korenkevych, D., Vasan, G., Ma, W., Bergstra, J.: Benchmarking reinforcement learning algorithms on real-world robots, CoRR, vol. abs/1809.07731. [Online]. Available: arXiv:1809.07731 (2018) – reference: Laskin, M., Srinivas, A., Abbeel, P.: Curl: contrastive unsupervised representations for reinforcement learning. In: International Conference on Machine Learning, pp 5639–5650. PMLR (2020) – reference: PfitscherMWelferDdo NascimentoEJCuadrosMAdSLGamarraDFTArticle users activity gesture recognition on kinect sensor using convolutional neural networks and fastdtw for controlling movements of a mobile robotIntel. Artif.2019226312113410.4114/intartif.vol22iss63pp121-134 – reference: da Silva, R.M., de Souza Leite Cuadros, M.A., Gamarra, D.F.T.: Comparison of a backstepping and a fuzzy controller for tracking a trajectory with a mobile robot. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds.) Intelligent Systems Design and Applications, pp 212–221. Cham, Springer International Publishing (2020) – reference: Achiam, J.: Spinning up in deep reinforcement learning, GitHub repository. [Online]. Available: https://github.com/openai/spinningup (2018) – reference: PyoYChoHJungRLimTRos Robot Programming2015ROBOTIS CoSeoul – reference: SuttonRSBartoAGReinforcement Learning: an Introduction2018CambridgeMIT Press1407.68009 – reference: KoberJBagnellJAPetersJReinforcement learning in robotics: a surveyInt. J. Robot. Res.201332111238127410.1177/0278364913495721 – reference: Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.A.: Playing atari with deep reinforcement learning, [Online]. Available: arXiv:1312.5602 (2013) – reference: Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D.: Continuous control with deep reinforcement learning. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. [Online]. Available: arXiv:1509.02971 (2016) – reference: Gu, S., Lillicrap, T., Sutskever, I., Levine, S.: Continuous deep q-learning with model-based acceleration. In: International Conference on Machine Learning, pp 2829–2838 (2016) – reference: Zhelo, O., Zhang, J., Tai, L., Liu, M., Burgard, W.: Curiosity-driven exploration for mapless navigation with deep reinforcement learning, [Online]. Available: arXiv:1804.00456 (2018) – reference: Dai, Z., Yang, Z., Yang, Y., Carbonell, J.G., Le, Q.V., Salakhutdinov, R.: Transformer-xl: attentive language models beyond a fixed-length context, [Online]. Available: arXiv:1901.02860 (2019) – reference: de Assis Brasil, P.M., Pereira, F.U., de Souza Leite Cuadros, M.A., Cukla, A.R., Tello Gamarra, D.F.: A study on global path planners algorithms for the simulated turtlebot 3 robot in ros. In: 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), pp 1–6 (2020) – reference: Tai, L., Liu, M.: Towards cognitive exploration through deep reinforcement learning for mobile robots, CoRR, vol. abs/1610.01733. [Online]. Available: arXiv:1610.01733 (2016) – reference: UhlenbeckGEOrnsteinLSOn the theory of the brownian motionPhys. Rev.193036582310.1103/PhysRev.36.823 – reference: Gu, S., Holly, E., Lillicrap, T., Levine, S.: Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp 3389–3396. IEEE (2017) – reference: Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., Levine, S.: Soft actor-critic algorithms and applications, [Online]. Available: arXiv:1812.05905 (2018) – reference: Jesus, J.C., Bottega, J.A., Cuadros, M.A., Gamarra, D.F.: Deep deterministic policy gradient for navigation of mobile robots in simulated environments. In: 2019 19th International Conference on Advanced Robotics (ICAR), pp 362–367. IEEE (2019) – reference: Omara, H.I.M.A., Sahari, K.S.M.: Indoor mapping using kinect and ros. In: 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR), pp 110–116. IEEE (2015) – reference: Chen, Y.F., Everett, M., Liu, M., How, J.P.: Socially aware motion planning with deep reinforcement learning. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 1343–1350. IEEE (2017) – reference: Schulman, J., Moritz, P., Levine, S., Jordan, M.I., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. [Online]. Available: arXiv:1506.02438 (2016) – reference: Subramanian, V.: Deep learning with pytorch: a practical approach to building neural network models using PyTorch Packt. Publishing Ltd (2018) – volume: 30 start-page: 838 issue: 4 year: 1992 ident: 1367_CR26 publication-title: SIAM J. Control Optim. doi: 10.1137/0330046 – ident: 1367_CR9 doi: 10.1186/s40638-016-0055-x – ident: 1367_CR33 – ident: 1367_CR29 – ident: 1367_CR17 doi: 10.1609/aaai.v30i1.10295 – ident: 1367_CR35 – ident: 1367_CR7 – ident: 1367_CR10 doi: 10.1109/IROS.2017.8202134 – ident: 1367_CR1 – ident: 1367_CR3 – ident: 1367_CR31 – volume: 11 start-page: 219 issue: 3-4 year: 2018 ident: 1367_CR15 publication-title: Found. Trends Mach. Learn. doi: 10.1561/2200000071 – ident: 1367_CR37 – ident: 1367_CR18 – ident: 1367_CR5 doi: 10.1109/ICRA.2017.7989385 – ident: 1367_CR36 doi: 10.1109/LARS/SBR/WRE51543.2020.9307003 – ident: 1367_CR16 – volume: 32 start-page: 1238 issue: 11 year: 2013 ident: 1367_CR20 publication-title: Int. J. Robot. Res. doi: 10.1177/0278364913495721 – ident: 1367_CR24 – ident: 1367_CR12 doi: 10.1109/ICAR46387.2019.8981638 – ident: 1367_CR11 doi: 10.1109/IROS.2017.8202312 – ident: 1367_CR6 – volume-title: Reinforcement Learning: an Introduction year: 2018 ident: 1367_CR38 – ident: 1367_CR4 – volume-title: Ros Robot Programming year: 2015 ident: 1367_CR34 – ident: 1367_CR2 – ident: 1367_CR28 doi: 10.1007/978-3-030-16660-1_21 – ident: 1367_CR30 doi: 10.1109/HPCA.2019.00048 – ident: 1367_CR32 doi: 10.18653/v1/P19-1285 – ident: 1367_CR19 – volume: 518 start-page: 529 issue: 7540 year: 2015 ident: 1367_CR22 publication-title: Nature doi: 10.1038/nature14236 – volume: 2 start-page: 71 issue: 4 year: 2014 ident: 1367_CR13 publication-title: Int. J. Intell. Syst. Appl. Eng. doi: 10.18201/ijisae.08103 – ident: 1367_CR21 – ident: 1367_CR8 doi: 10.1109/ICRA.2017.7989381 – ident: 1367_CR25 – volume: 36 start-page: 823 issue: 5 year: 1930 ident: 1367_CR23 publication-title: Phys. Rev. doi: 10.1103/PhysRev.36.823 – ident: 1367_CR14 doi: 10.1109/ISAMSR.2015.7379780 – volume: 22 start-page: 121 issue: 63 year: 2019 ident: 1367_CR27 publication-title: Intel. Artif. doi: 10.4114/intartif.vol22iss63pp121-134
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SubjectTerms	Algorithms Angular velocity Artificial Intelligence Comparative analysis Control Electrical Engineering Employee motivation Engineering Mechanical Engineering Mechatronics Navigation Regular Paper Robotics Robots Topical collection on ICAR 2019 Special Issue
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Title	Soft Actor-Critic for Navigation of Mobile Robots
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