First results with Dyna, an integrated architecture for learning, planning and reacting
Neural networks for control
Technical Note: \cal Q-Learning
Machine Learning
Numerical methods for stochastic control problems in continuous time
Numerical methods for stochastic control problems in continuous time
Robot Motion Planning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Variable Resolution Discretization in Optimal Control
Machine Learning
Reinforcement Learning to Drive a Car by Pattern Matching
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Motion Planning of a Non-holonomic Vehicle in a Real Environment by Reinforcement Learning*
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A geometric algorithm to compute time-optimal trajectories for a bidirectional steered robot
IEEE Transactions on Robotics
Reactive path planning in a dynamic environment
IEEE Transactions on Robotics
Control of nonholonomic mobile robots based on the transverse function approach
IEEE Transactions on Robotics
Provably Efficient Learning with Typed Parametric Models
The Journal of Machine Learning Research
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The aim of this work has been the implementation and testing in real conditions of a new algorithm based on the cell-mapping techniques and reinforcement learning methods to obtain the optimal motion planning of a vehicle considering kinematics, dynamics and obstacle constraints. The algorithm is an extension of the control adjoining cell mapping technique for learning the dynamics of the vehicle instead of using its analytical state equations. It uses a transformation of cell-to-cell mapping in order to reduce the time spent during the learning stage. Real experimental results are reported to show the satisfactory performance of the algorithm.