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Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball
Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball
Master's Theses
Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum …
Mastering The Game Of Gomoku Without Human Knowledge, Yuan Wang
Mastering The Game Of Gomoku Without Human Knowledge, Yuan Wang
Master's Theses
Gomoku, also called Five in a row, is one of the earliest checkerboard games invented by humans. For a long time, it has brought countless pleasures to us. We humans, as players, also created a lot of skills in playing it. Scientists normalize and enter these skills into the computer so that the computer knows how to play Gomoku. However, the computer just plays following the pre-entered skills, it doesn’t know how to develop these skills by itself. Inspired by Google’s AlphaGo Zero, in this thesis, by combining the technologies of Monte Carlo Tree Search, Deep Neural Networks, and Reinforcement …