Learning to Play Using Low-Complexity Rule-Based Policies: Illustrations through Ms. Pac-Man
نویسندگان
چکیده
منابع مشابه
Learning to Play Using Low-Complexity Rule-Based Policies: Illustrations through Ms. Pac-Man
In this article we propose a method that can deal with certain combinatorial reinforcement learning tasks. We demonstrate the approach in the popular Ms. Pac-Man game. We define a set of high-level observation and action modules, from which rule-based policies are constructed automatically. In these policies, actions are temporally extended, and may work concurrently. The policy of the agent is...
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Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. This paper describes an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset, with parameters that control the probability of movement by the agent given the constra...
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In this paper we propose a method that learns to play Pac-Man. We define a set of high-level observation and action modules. Actions are temporally extended, and multiple action modules may be in effect concurrently. A decision of the agent is represented as a rule-based policy. For learning, we apply the cross-entropy method, a recent global optimization algorithm. The learned policies reached...
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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 dyn...
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Conventional reinforcement learning methods for Markov decision processes rely on weakly-guided, stochastic searches to drive the learning process. It can therefore be difficult to predict what agent behaviors might emerge. In this paper, we consider an information-theoretic cost function for performing constrained stochastic searches that promote the formation of risk-averse to risk-favoring b...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2007
ISSN: 1076-9757
DOI: 10.1613/jair.2368