Date of Award
2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Complex Systems
First Advisor
Joshua Bongard
Second Advisor
Nicholas Cheney
Abstract
Agents are often trained to perform a task via optimization algorithms. One class of algorithms used is evolution, which is ``survival of the fitness'' used to pick the best agents for the objective, and slowly changing the best over time to find a good solution. Evolution, or evolutionary algorithms, have been commonly used to automatically select for a better body of the agent, which can outperform hand-designed models. Another class of algorithms used is reinforcement learning. Through this strategy, agents learn from prior experiences in order to maximize some reward. Generally, this reward is how close the objective is to being complete, or otherwise some stepping stone towards its completion. Evolution and reinforcement learning can both train agents to the point where a task can be successfully completed, or an objective met. In this thesis, we outline a framework for combining evolution and reinforcement learning, and outline experimental designs to test this method against traditional reinforcement learning. Finally, we show preliminary results as a proof of concept for the methods described.
Language
en
Number of Pages
24 p.
Recommended Citation
Felag, Jack, "Co-optimization of a Robot's Body and Brain via Evolution and Reinforcement Learning" (2020). Graduate College Dissertations and Theses. 1224.
https://scholarworks.uvm.edu/graddis/1224