TY - GEN
T1 - A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces
AU - Rousseas, Panagiotis
AU - Bechlioulis, Charalampos P.
AU - Kyriakopoulos, Kostas J.
N1 - Funding Information:
VIII. ACKNOWLEDGEMENTS This work was supported by the European Union’s Horizon 2020 Research and Innovation Program PATHOCERT - Pathogen Contamination Emergency Response Technologies under Grant 883484.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT∗ method in path length, cost function values and execution times, indicating almost global optimality.
AB - In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT∗ method in path length, cost function values and execution times, indicating almost global optimality.
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U2 - 10.1109/ICRA48891.2023.10161189
DO - 10.1109/ICRA48891.2023.10161189
M3 - Conference contribution
AN - SCOPUS:85168657130
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 10247
EP - 10253
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
Y2 - 29 May 2023 through 2 June 2023
ER -