Dissertation
Enhancing Autonomy and Efficiency in Goal-Conditioned Reinforcement Learning
Reinforcement learning is a framework that enables agents to learn in a manner similar to humans, i.e. through trial and error. Ideally, we would like to train a generalist agent capable of performing multiple tasks and achieving various goals.
- Author
- Z. Yang
- Date
- 26 February 2025
- Links
- Thesis in Leiden Repository
Goal-conditioned reinforcement learning is a step toward training such an agent. The goal-conditioned reinforcement learning framework comprises four steps: 1) defining the goal space; 2) selecting an interesting goal for the agent; 3) the agent learning to reach the goal; 4) the agent post-exploring. In the thesis, we proposed four research questions and focused on three parts of the goal-conditioned reinforcement learning framework.