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Showing result 1 - 5 of 7 essays matching the above criteria.
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1. Evaluating the performance of a team consisting of an advanced agent and a less advanced agent in the game Manille : A comparison of agents trained using the CFR algorithm with and without abstractions.
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Making artificial intelligence (AI) algorithms solve games has always been an interesting benchmark of AI research. Perfect information games like Chess can be played on a level beyond human capabilities. READ MORE
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2. Self-Play Reinforcement Learning for Finding Intrusion Prevention Strategies
University essay from KTH/Matematisk statistikAbstract : This Master thesis studies automated intrusion prevention using self-play reinforcement learning. We extend a decision-theoretic model of the intrusion prevention use case based on optimal stopping theory proposed in previous work to a game-theoretic setting. READ MORE
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3. Deep Reinforcement LearningA case study of AlphaZero
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Using deep neural networks for reinforcement learning has proven very successful, as demonstrated by the AlphaZero algorithm developed by DeepMind in 2018. This algorithm is capable of mastering two-player zero-sum board games entirely by playing against itself. READ MORE
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4. Deep Distributional Temporal Difference Learning for Game Playing
University essay from Lunds universitet/Matematisk statistikAbstract : Temporal difference learning is considered one of the most successful methods in reinforcement learning. Recent developments in deep learning have opened up a new world of opportunities. READ MORE
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5. Game contingent claims
University essay from KTH/Matematisk statistikAbstract : Abstract Game contingent claims (GCCs), as introduced by Kifer (2000), are a generalization of American contingent claims where the writer has the opportunity to terminate the contract, and must then pay the intrinsic option value plus a penalty. In complete markets, GCCs are priced using no-arbitrage arguments as the value of a zero-sum stochastic game of the type described in Dynkin (1969). READ MORE
