Fast adaptation via meta reinforcement learning - Zintgraf, L (2022)
Reinforcement Learning (RL) is a way to train artificial agents to autonomously interact with the world. In practice however, RL still has limitations that prohibit the deployment of RL agents in many real world settings. This is because RL takes long, typically requires human oversight, and produces specialised agents that can behave unexpected in unfamiliar situations. This thesis is motivated by the goal of making RL agents more flexible, robust, and safe to deploy in the real world. We develop agents capable of Fast Adaptation, i.e., agents that can learn new tasks efficiently.