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Play Ms. Pac-Man Using an Advanced Reinforcement Learning Agent

  • Nikolaos Tziortziotis
  • Konstantinos Tziortziotis
  • Konstantinos Blekas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8445)

Abstract

Reinforcement Learning (RL) algorithms have been promising methods for designing intelligent agents in games. Although their capability of learning in real time has been already proved, the high dimensionality of state spaces in most game domains can be seen as a significant barrier. This paper studies the popular arcade video game Ms. Pac-Man and outlines an approach to deal with its large dynamical environment. Our motivation is to demonstrate that an abstract but informative state space description plays a key role in the design of efficient RL agents. Thus, we can speed up the learning process without the necessity of Q-function approximation. Several experiments were made using the multiagent MASON platform where we measured the ability of the approach to reach optimum generic policies which enhances its generalization abilities.

Keywords

Intelligent Agents Reinforcement Learning Ms. Pac-Man 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nikolaos Tziortziotis
    • 1
  • Konstantinos Tziortziotis
    • 1
  • Konstantinos Blekas
    • 1
  1. 1.Department of Computer Science & EngineeringUniversity of IoanninaIoanninaGreece

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