M. Gopal

Bio: M. Gopal is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Markov decision process & Fictitious play. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Fictitious play based Markov game control for robotic arm manipulator

Abstract: Markov games can be used as a platform to deal with exogenous disturbances and parametric variations. In this work an attempt has been made to achieve a superior performance with fuzzy Markov game based control by hybridizing two game theory based approaches of ‘fictitious play’ and ‘minimax’. The work attempts a ‘safe yet consistent’ Markov game controller which advocates a minimax policy during the startup control stages and later moves to a more enterprising policy based on stochastic fictitious play. The proposed controller addresses continuous state action space problems wherein we use fuzzy inference system as a universal approximator for generalization with a proportional derivative control in the nested position tracking loop. The proposed controller is simulated on a two link robot and its performance compared against fuzzy Markov game control and fuzzy Q control. Simulation results elucidate the fact that proposed control scheme leads to an improved controller with lower tracking error and torque requirements.

Analytical and experimental nonzero-sum differential game-based control of a 7-DOF robotic manipulator:

Abstract: We formulate a Nash-based feedback control law for an Euler–Lagrange system to yield a solution to noncooperative differential game. The robot manipulators are broadly used in industrial units on t...

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

Abstract: This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.