Reinforcement Learning Using Neural Networks, with Applications to Motor Control. (Apprentissage par renforcement utilisant des réseaux de neurones, avec des applications au contrôle moteur)

This thesis is a study of practical methods to estimate value functions with feedforward neural networks in model-based reinforcement learning. Focus is placed on problems in continuous time and space, such as motor-control tasks. In this work, the continuous TD(lambda) algorithm is refined to handle situations with discontinuous states and controls, and the vario-eta algorithm is proposed as a simple but efficient method to perform gradient descent. The main contributions of this thesis are experimental successes that clearly indicate the potential of feedforward neural networks to estimate high-dimensional value functions. Linear function approximators have been often preferred in reinforcement learning, but successful value function estimations in previous works are restricted to mechanical systems with very few degrees of freedom. The method presented in this thesis was tested successfully on an original task of learning to swim by a simulated articulated robot, with 4 control variables and 12 independent state variables, which is significantly more complex than problems that have been solved with linear function approximators so far.

[1]  F. H. Adler Cybernetics, or Control and Communication in the Animal and the Machine. , 1949 .

[2]  Ronald A. Howard,et al.  Dynamic Programming and Markov Processes , 1960 .

[3]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[4]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[6]  Y. L. Cun Learning Process in an Asymmetric Threshold Network , 1986 .

[7]  S. V. Emel'yanov,et al.  Higher-order sliding modes in binary control systems , 1986 .

[8]  Yann LeCun,et al.  Learning processes in an asymmetric threshold network , 1986 .

[9]  Charles W. Anderson,et al.  Strategy Learning with Multilayer Connectionist Representations , 1987 .

[10]  Scott E. Fahlman,et al.  An empirical study of learning speed in back-propagation networks , 1988 .

[11]  Aleksej F. Filippov,et al.  Differential Equations with Discontinuous Righthand Sides , 1988, Mathematics and Its Applications.

[12]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[13]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[14]  William H. Press,et al.  Numerical recipes in C++: the art of scientific computing, 2nd Edition (C++ ed., print. is corrected to software version 2.10) , 1994 .

[15]  Todd K. Leen,et al.  Weight Space Probability Densities in Stochastic Learning: II. Transients and Basin Hopping Times , 1992, NIPS.

[16]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[17]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[18]  Christopher G. Atkeson,et al.  Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming , 1993, NIPS.

[19]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[20]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[21]  Leemon C Baird,et al.  Reinforcement Learning With High-Dimensional, Continuous Actions , 1993 .

[22]  Andrew W. Moore,et al.  Generalization in Reinforcement Learning: Safely Approximating the Value Function , 1994, NIPS.

[23]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[24]  Michael I. Jordan,et al.  MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES , 1996 .

[25]  J. Shewchuk An Introduction to the Conjugate Gradient Method Without the Agonizing Pain , 1994 .

[26]  Karl Sims,et al.  Evolving 3D Morphology and Behavior by Competition , 1994, Artificial Life.

[27]  S. Schaal,et al.  Robot juggling: implementation of memory-based learning , 1994, IEEE Control Systems.

[28]  Barak A. Pearlmutter Fast Exact Multiplication by the Hessian , 1994, Neural Computation.

[29]  Gerald Tesauro,et al.  Temporal Difference Learning and TD-Gammon , 1995, J. Int. Comput. Games Assoc..

[30]  Geoffrey J. Gordon Stable Function Approximation in Dynamic Programming , 1995, ICML.

[31]  Mark W. Spong,et al.  The swing up control problem for the Acrobot , 1995 .

[32]  Kenji Doya,et al.  Temporal Difference Learning in Continuous Time and Space , 1995, NIPS.

[33]  Thomas G. Dietterich,et al.  High-Performance Job-Shop Scheduling With A Time-Delay TD(λ) Network , 1995, NIPS 1995.

[34]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[35]  Richard S. Sutton,et al.  Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding , 1995, NIPS.

[36]  Andrew G. Barto,et al.  Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..

[37]  Leemon C. Baird,et al.  Residual Algorithms: Reinforcement Learning with Function Approximation , 1995, ICML.

[38]  Todd K. Leen,et al.  Using Curvature Information for Fast Stochastic Search , 1996, NIPS.

[39]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[40]  John N. Tsitsiklis,et al.  Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.

[41]  John N. Tsitsiklis,et al.  Analysis of Temporal-Diffference Learning with Function Approximation , 1996, NIPS.

[42]  Ralph Neuneier,et al.  How to Train Neural Networks , 1996, Neural Networks: Tricks of the Trade.

[43]  Gary Boone,et al.  Efficient reinforcement learning: model-based Acrobot control , 1997, Proceedings of International Conference on Robotics and Automation.

[44]  Ashwin Ram,et al.  Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces , 1997, Adapt. Behav..

[45]  Stephan Pareigis,et al.  Adaptive Choice of Grid and Time in Reinforcement Learning , 1997, NIPS.

[46]  Christopher G. Atkeson,et al.  A comparison of direct and model-based reinforcement learning , 1997, Proceedings of International Conference on Robotics and Automation.

[47]  Gary Boone,et al.  Minimum-time control of the Acrobot , 1997, Proceedings of International Conference on Robotics and Automation.

[48]  Rémi Munos Apprentissage par renforcement, étude du cas continu , 1997 .

[49]  Rémi Munos,et al.  A Convergent Reinforcement Learning Algorithm in the Continuous Case Based on a Finite Difference Method , 1997, IJCAI.

[50]  Jun Morimoto,et al.  Hierarchical Reinforcement Learning of Low-Dimensional Subgoals and High-Dimensional Trajectories , 1998, ICONIP.

[51]  Preben Alstrøm,et al.  Learning to Drive a Bicycle Using Reinforcement Learning and Shaping , 1998, ICML.

[52]  Andrew Tridgell,et al.  Experiments in Parameter Learning Using Temporal Differences , 1998, J. Int. Comput. Games Assoc..

[53]  Marios M. Polycarpou,et al.  Preventing unlearning during online training of feedforward networks , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

[54]  Marios M. Polycarpou,et al.  An analytical framework for local feedforward networks , 1998, IEEE Trans. Neural Networks.

[55]  Andrew W. Moore,et al.  Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems , 1999, IJCAI.

[56]  Andrew W. Moore,et al.  Gradient descent approaches to neural-net-based solutions of the Hamilton-Jacobi-Bellman equation , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[57]  Yasuharu Koike,et al.  Multiple state estimation reinforcement learning for driving model: driver model of automobile , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[58]  Nicol N. Schraudolph,et al.  Local Gain Adaptation in Stochastic Gradient Descent , 1999 .

[59]  K. Kreutz-Delgado,et al.  Obtaining minimum energy biped walking gaits with symbolic models and numerical optimal control , 1999 .

[60]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.

[61]  Alexander Zelinsky,et al.  Q-Learning in Continuous State and Action Spaces , 1999, Australian Joint Conference on Artificial Intelligence.

[62]  Junichiro Yoshimoto,et al.  Application of reinforcement learning to balancing of Acrobot , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[63]  Michiel van de Panne,et al.  Control for Simulated Human and Animal Motion , 2000 .

[64]  Charles W. Anderson,et al.  Approximating a Policy Can be Easier Than Approximating a Value Function , 2000 .

[65]  Kenji Doya,et al.  Reinforcement Learning in Continuous Time and Space , 2000, Neural Computation.

[66]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[67]  Jun Morimoto,et al.  Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning , 2000, Robotics Auton. Syst..

[68]  John N. Tsitsiklis,et al.  On the Convergence of Optimistic Policy Iteration , 2002, J. Mach. Learn. Res..

[69]  Thomas G. Dietterich,et al.  Editors. Advances in Neural Information Processing Systems , 2002 .

[70]  Jean-Arcady Meyer,et al.  Evolutionary approaches to neural control of rolling, walking, swimming and flying animats or robots , 2003 .

[71]  Peter Dayan,et al.  The convergence of TD(λ) for general λ , 1992, Machine Learning.

[72]  Terrence J. Sejnowski,et al.  TD(λ) Converges with Probability 1 , 1994, Machine Learning.

[73]  John N. Tsitsiklis,et al.  Feature-based methods for large scale dynamic programming , 2004, Machine Learning.

[74]  Andrew G. Barto,et al.  Elevator Group Control Using Multiple Reinforcement Learning Agents , 1998, Machine Learning.

[75]  H. J. Pesch,et al.  Real-time Collision Avoidance against Wrong Drivers: Diierential Game Approach, Numerical Solution and Synthesis of Strategies with Neural Networks , 2022 .