Generalization plays a pivotal role in the realm of Reinforcement Learning. While RL algorithms demonstrate good performance in controlled environments, the real world presents a unique challenge due to its non-stationary and open-ended nature.
As a result, the development of RL algorithms that stay robust in the face of environmental variations, coupled with the capability to transfer and adapt to uncharted yet analogous tasks and settings, becomes fundamental for real world application of RL.
If you’re interested to dive deeper into this research subject, we recommend exploring the following resource:
Generalization in Reinforcement Learning by Robert Kirk: this comprehensive survey provides an insightful overview of the concept of generalization in RL, making it an excellent starting point for your exploration.