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Proceedings ArticleDOI

Fictitious play based Markov game control for robotic arm manipulator

TLDR
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.
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.

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Citations
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Journal ArticleDOI

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

TL;DR: A Nash-based feedback control law is formulated for an Euler–Lagrange system to yield a solution to noncooperative differential game.
Book ChapterDOI

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

TL;DR: In this paper, a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players' strategies, which can be used as a coordination mechanism between players when they should take decisions under uncertainty.
References
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Journal ArticleDOI

Learning to Predict by the Methods of Temporal Differences

Richard S. Sutton
- 01 Aug 1988 - 
TL;DR: This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior – and proves their convergence and optimality for special cases and relation to supervised-learning methods.
Book

The theory of learning in games

TL;DR: Fudenberg and Levine as discussed by the authors developed an alternative explanation that equilibrium arises as the long-run outcome of a process in which less than fully rational players grope for optimality over time.
Book ChapterDOI

Markov games as a framework for multi-agent reinforcement learning

TL;DR: A Q-learning-like algorithm for finding optimal policies and its application to a simple two-player game in which the optimal policy is probabilistic is demonstrated.
BookDOI

Handbook of Learning and Approximate Dynamic Programming

TL;DR: This chapter discusses reinforcement learning in large, high-dimensional state spaces, model-based adaptive critic designs, and applications of approximate dynamic programming in power systems control.
Proceedings ArticleDOI

Fuzzy Q-learning

TL;DR: This paper proposes an adaptation of Watkins' Q-learning for fuzzy inference systems where both the actions and the Q-functions are inferred from fuzzy rules, showing its effectiveness.
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