Theoretical Research in Planning Simulations using Reinforcement Learning

Transfer learning is the task of transferring knowledge from one source to another. In this case,
we compare our algorithm to policy distillation, which involves training a teacher DQN in an
environment. This teacher is then used to produce a dataset of states and their associated
Q-values according to the target network and distill this knowledge to the target agent.

Project Report