Remedying this weakness is a key challenge in the quest for building intelligent agents that can learn continually when deployed in the real world, where their experiences are not necessarily i.i.d. and their resources may be limited. In my PhD, I have studied catastrophic forgetting in the context of deep reinforcement learning, where changes to the distribution of an agent's experiences arise from multiple sources and occur unpredictably over the course of learning. Inspired partially by the processes of synaptic consolidation and systems consolidation in the brain, I will present two methods that harness multi-timescale processes to mitigate catastrophic forgetting in an RL setting. Bio: Christos is currently pursuing a PhD on the topic of Continual Reinforcement Learning at Imperial College London, co-supervised by Claudia Clopath (Bioengineering) and Murray Shanahan (Computing). He graduated with a BA in Applied Mathematics from Harvard and worked as a trader at Brevan Howard for several years, before leaving to pursue MScs in Computing and Informatics at Imperial College and Edinburgh University respectively, driven by an interest in computational neuroscience and machine learning. In April, he will start a job as a Research Scientist at DeepMind.