The broad problem I address in this dissertation is the design of autonomous agents that can efficiently learn goal-directed behavior in sequential decision-making problems under uncertainty. I investigate how certain models of bounded rationality—simple decision-making models that take into consideration the limited cognitive abilities of biological and artificial minds---can inform reinforcement learning algorithms to produce more resource-efficient agents. In the two main parts of this dissertation I use different existing models of bounded rationality to address different resource limitations present in sequential decision-making problems. In the first part I introduce a boundedly rational function approximation architecture for reinforcement learning agents to reduce the amount of training data required to learn a useful behavioral policy. In the second part I investigate how Herbert A. Simon's satisficing strategy can be applied in sequential decision making problems to reduce the computational effort of the action-selection process.
- reinforcement learning
- bounded rationality
- machine learning
- artificial intelligence
Bounded Rationality in Reinforcement Learning: (Alternative Format Thesis)
Lichtenberg, J. M. (Author). 29 Mar 2023
Student thesis: Doctoral Thesis › PhD