In DRL, we can refer to transfer learning as the ability to use knowledge gained while training an agent in one domain and applying it to the training of another agent, usually in a different domain.
By transferring the weights of different parts of the networks, we sought to improve the learning rate and maximum reward achieved by the DQN algorithm.
