Another important topic that is increasing in importance and attention is transfer learning. Transfer learning is a paradigm in machine learning, where a model trained on one task is fine-tuned to accomplish another.
For example, we can train a model to recognize images of cars and use the weights of that model to initialize an identical model that learns to recognize trucks. The main intuition is that certain abstract concepts and features learned by training on one task are transferable to other similar tasks. This idea is applicable to many reinforcement learning problems as well. An agent that learns to play a particular Atari game should be able to play other Atari games proficiently without training entirely from scratch, ...
