A new evolutionary reinforcement scheme for stochastic learning automata

A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. The aim is to design an automaton, using an evolutionary reinforcement scheme (the basis of the learning process), that can determine the best action guided by past actions and responses. Using Stochastic Learning Automata techniques, we introduce a decision/control method for intelligent vehicles receiving data from on-board sensors or from the localization system of highway infrastructure.

[2]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[3]  Pushkin Kachroo,et al.  Multiple stochastic learning automata for vehicle path control in an automated highway system , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Yufeng Liu,et al.  Stochastic Direct Reinforcement: Application to Simple Games with Recurrence , 2004, AAAI Technical Report.

[5]  Pushkin Kachroo,et al.  Simulation study of multiple intelligent vehicle control using stochastic learning automata , 1997 .

[6]  Sridhar Mahadevan,et al.  Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..

[7]  Hajime Kita,et al.  A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms , 2001, Evolutionary Computation.

[8]  Olivier Buffet,et al.  Incremental reinforcement learning for designing multi-agent systems , 2001, AGENTS '01.

[9]  S. Lakshmivarahan,et al.  Absolutely Expedient Learning Algorithms For Stochastic Automata , 1973 .

[10]  Kumpati S. Narendra,et al.  Learning automata - an introduction , 1989 .

[11]  A. Iazzetta.  The Eeectiveness of Co{mutation in Evolutionary Algorithms: the M Ijn Operator , 2022 .

[12]  E. Tarantino,et al.  The effectiveness of co-mutation in evolutionary algorithms: the /spl Mscr//sub ijn/ operator , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[13]  Stamatios V. Kartalopoulos,et al.  Proceedings of the 12th WSEAS international conference on Computers , 2008 .

[14]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[15]  Ernesto Tarantino,et al.  A Comparative Analysis of Evolutionary Algorithms for Function Optimisation , 1996 .