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Play a tricky spaceship piloting and shooting arcade game inspired by the 1970s & 80s classic space shooter game ‘Asteroids’ on your mobile phone, tablet, notebook, laptop, or desktop PC! Shoot Asteroids is a very challenging, space invaders-style skill game where you must pilot a spaceship through a really high-intensity field of asteroids and (smaller) meteoroids, destroying as many space rocks and UFOs as you can with concentrated missile fire. Dodge and weave around the slow-moving asteroids & meteoroids, collect gold discs and score as many points as you can! Use the teleport function to escape from sticky situations. This game is great for sci-fi and retro game fans.
This online, HTML5-based, space invaders / survival skill game should work on iOS and Android devices as well as most web browsers for PC / Mac. Skills required here include focus, alertness, sharp reflexes and anticipation skills, good hand-eye coordination, great determination and will to win!
Control Tips: For laptop, notebook or desktop players, using the Arrow Keys on your keyboard to control the spaceship and Spacebar to shoot and Shift button to Teleport. For mobile phone & tablet players, please connect USB or Bluetooth keyboard to play the game. Enjoy this old-school space arcade challenge!
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Title: Bootstrapping learning from abstract models in games
Authors: Purvag Patel; Normal Carver; Shahram Rahimi
Addresses: Department of Computer Science, Southern Illinois University, Carbondale, IL, 62901, USA ' Department of Computer Science, Southern Illinois University, Carbondale, IL, 62901, USA ' Department of Computer Science, Southern Illinois University, Carbondale, IL, 62901, USA
Abstract: Computer gaming environments are real time, dynamic, and complex, with incomplete knowledge of the world. Agents in such environments require detailed models of the world if they are to learn effective policies. Machine learning techniques such as reinforcement learning can become intractably large, detailed world models. In this paper we tackle the well-known problem of low convergence speed in reinforcement learning for the detailed model of the world, specifically for video games. We propose first training the agents with an abstract model of the world and then using the resulting policy to initialise the system prior to training the agent with the detailed model of the world. This paper reports on results from applying the proposed technique to the classic arcade game Asteroids. Our experiments show that an agent can quickly learn a policy with the abstract model, and that when this policy's learned values are used to initialise the detailed model, learning with the detailed model improves the rate of convergence.
Keywords: Q-learning; reinforcement learning; computer games; bots; convergence rate; bootstrapping; abstract models; modelling; video games; gaming; arcade games; Asteroids; learning agents; multi-agent systems; MAS; agent-based systems.
DOI: 10.1504/IJBIC.2013.055452
International Journal of Bio-Inspired Computation, 2013 Vol.5 No.4, pp.239 - 251
Asteroids Game Asteroid
Received: 17 Nov 2012
Accepted: 20 Nov 2012
Published online: 29 Jul 2013*
