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Showing result 11 - 15 of 16 essays matching the above criteria.
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11. Using Reinforcement Learning for Games with Nondeterministic State Transitions
University essay from Linköpings universitet/Statistik och maskininlärningAbstract : Given the recent advances within a subfield of machine learning called reinforcement learning, several papers have shown that it is possible to create self-learning digital agents, agents that take actions and pursue strategies in complex environments without any prior knowledge. This thesis investigates the performance of the state-of-the-art reinforcement learning algorithm proximal policy optimization, when trained on a task with nondeterministic state transitions. READ MORE
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12. Intelligent Formation Control using Deep Reinforcement Learning
University essay from Linköpings universitet/Artificiell intelligens och integrerade datorsystemAbstract : In this thesis, deep reinforcement learning is applied to the problem of formation control to enhance performance. The current state-of-the-art formation control algorithms are often not adaptive and require a high degree of expertise to tune. READ MORE
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13. Optimized Trade Execution with Reinforcement Learning
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : In this thesis, we study the problem of buying or selling a given volume of a financial asset within a given time horizon to the best possible price, a problem formally known as optimized trade execution. Our approach is an empirical one. We use historical data to simulate the process of placing artificial orders in a market. READ MORE
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14. Comminution control using reinforcement learning : Comparing control strategies for size reduction in mineral processing
University essay from Umeå universitet/Institutionen för fysikAbstract : In mineral processing the grinding comminution process is an integral part since it is often the bottleneck of the concentrating process, thus small improvements may lead to large savings. By implementing a Reinforcement Learning controller this thesis aims to investigate if it is possible to control the grinding process more efficiently compared to traditional control strategies. READ MORE
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15. Curriculum learning for increasing the performance of a reinforcement learning agent in a static first-person shooter game
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In this thesis, we trained a reinforcement learning agent using one of the most recent policy gradient methods, proximal policy optimization, in a first-person shooter game with a static player. We investigated how curriculum learning can be used to increase performance of a reinforcement learning agent. READ MORE
