Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 7 of 7 for “"Atari games"”.
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Beating the world's best at Super Smash Bros. with deep reinforcement learning
… intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now solvable by fairly generic algorithms, based on deep learning, that learn to play from experience with often minimal knowledge of the specific domain of interest. In this work, we will investigate …
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Inferring Structured World Models from Videos
… have allowed agents to learn a variety of board games and video games at superhuman levels. Unlike humans - which can generalize to a wide range of tasks with very little experience - these algorithms typically need vast number of experience replays to perform at the same level. In this thesis, …
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ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning
… and Proximal Policy Optimization (PPO), under Atari games and MuJoCo where both targeted and non-targeted attacks are considered with or without the state-of-the-art defenses in DRL (i.e., RADIAL and ATLA). Our results demonstrate that the proposed ACADIA outperforms existing gradient-based …
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Optimizing Verification of RTL Designs Using Reinforcement Learning Methods
… in the work by DeepMind Technologies to play Atari games. The RTL design used throughout the research is an LZW encoder as it is on one hand sufficiently complex to be representative, and on the other hand, it allows for an unambiguous assessment of the novel approach on the functional …
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Towards Intelligent Videogame Generation
… remains a resource-intensive endeavor. Videogames are complex soft real-time systems that necessitate meticulous modeling of 3D objects, animations, graphical effects, physics, and intelligent agents. The collaborative efforts of various specialized professionals, including designers, …
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Generic Reinforcement Learning Beyond Small MDPs
Feature reinforcement learning (FRL) is a framework within which an agent can automatically reduce a complex environment to a Markov Decision Process (MDP) by finding a map which aggregates similar histories into the states of an MDP. The primary motivation behind this thesis is to build FRL agents …
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Generic Reinforcement Learning Beyond Small MDPs
Feature reinforcement learning (FRL) is a framework within which an agent can automatically reduce a complex environment to a Markov Decision Process (MDP) by finding a map which aggregates similar histories into the states of an MDP. The primary motivation behind this thesis is to build FRL agents …