Entombed: An archaeological examination of an Atari 2600 game

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چکیده

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Learning from the memory of Atari 2600

We train a number of neural networks to play games Bowling, Breakout and Seaquest using information stored in the memory of a video game console Atari 2600. We consider four models of neural networks which differ in size and architecture: two networks which use only information contained in the RAM and two mixed networks which use both information in the RAM and information from the screen. As ...

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Investigating Contingency Awareness Using Atari 2600 Games

Contingency awareness is the recognition that some aspects of a future observation are under an agent’s control while others are solely determined by the environment. This paper explores the idea of contingency awareness in reinforcement learning using the platform of Atari 2600 games. We introduce a technique for accurately identifying contingent regions and describe how to exploit this knowle...

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The Impact of Determinism on Learning Atari 2600 Games

Pseudo-random number generation on the Atari 2600 was commonly accomplished using a Linear Feedback Shift Register (LFSR). One drawback was that the initial seed for the LFSR had to be hard-coded into the ROM. To overcome this constraint, programmers sampled from the LFSR once per frame, including title and end screens. Since a human player will have some random amount of delay between seeing t...

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Pairwise Relative Offset Features for Atari 2600 Games

We introduce a novel feature set for reinforcement learning in visual domains (e.g. video games) designed to capture pairwise, position-invariant, spatial relationships between objects on the screen. The feature set is simple to implement and computationally practical, but nevertheless allows for substantial improvement over existing baselines in a wide variety of Atari 2600 games. In the most ...

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ژورنال

عنوان ژورنال: The Art, Science, and Engineering of Programming

سال: 2018

ISSN: 2473-7321

DOI: 10.22152/programming-journal.org/2019/3/4