Degree
Doctor of Philosophy (PhD)
Department
Computer Science
Document Type
Dissertation
Abstract
In the field of reinforcement learning, robot task learning in a specific environment with a Markov decision process backdrop has seen much success. But, extending these results to learning a task for an environment domain has not been as fruitful, even for advanced methodologies such as relational reinforcement learning. In our research into robot learning in environment domains, we utilize a form of deictic representation for the robot’s description of the task environment. However, the non-Markovian nature of the deictic representation leads to perceptual aliasing and conflicting actions, invalidating standard reinforcement learning algorithms. To circumvent this difficulty, several past research studies have modified and extended the Q-learning algorithm to the deictic representation case with mixed results. Taking a different tact, we introduce a learning algorithm which searches deictic policy space directly, abandoning the indirect value based methods. We apply the policy learning algorithm to several different tasks in environment domains. The results compare favorably with value based learners and existing literature results.
Date
10-22-2018
Recommended Citation
Moore, Harry Paul, "Reinforcement Learning in Robotic Task Domains with Deictic Descriptor Representation" (2018). LSU Doctoral Dissertations. 4738.
https://repository.lsu.edu/gradschool_dissertations/4738
Committee Chair
Chen, Jianhua
DOI
10.31390/gradschool_dissertations.4738