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Learning Modes for Sequential Decision Making Using Stochastic Search

The objective of this project is to design and implement a stochastic search algorithm to allow an agent to explore a given state space. The state space is given in the Planning Domain Definition Language (PDDL) format containing potential actions and their preconditions and effects. The agent attempts to create a plan for a randomly

Learning Modes for Sequential Decision Making Using Stochastic Search

To develop plans and operate autonomously, robots need knowledge bases encoded in PDDL, the planning domain definition language. The purpose of this research is to explore learning methods to reduce the human supervision needed to acquire these knowledge bases. A reinforcement learning environment was developed to allow a learning agent to explore the meta-space of