Challenges for learning in complex environments
Raia T. Hadsell · Proceedings of the Genetic and Evolutionary Computation Conference · 2019
Deep reinforcement learning has rapidly grown as a research field with far-reaching potential for artificial intelligence. Games and simple physical simulations have been used as the main benchmark domains for many fundamental developments. As the field matures, it is important to develop more sophisticated learning systems with the aim of solving more complex real-world tasks, but problems like catastrophic forgetting remain critical, and important capabilities such as skill composition through curriculum learning remain unsolved. Continual learning is an important challenge for reinforcement learning, because RL agents are trained sequentially, in interactive environments, and are especially vulnerable to the phenomena of catastrophic forgetting and catastrophic interference. Successful methods for continual learning have broad potential, because they could enable agents to learn multiple skills, potentially enabling complex behaviors.