Continual Reinforcement Learning with TELLA

Training reinforcement learning agents that continually learn across multiple environments is a challenging problem. This is made more difficult by a lack of reproducible experiments and standard metrics for comparing different continual learning approaches. Researchers can define and share their own curricula over various learning environments or run against a curriculum created under the DARPA Lifelong Learning Machines (L2M) Program. In the last decade, reinforcement learning (RL) with deep neural networks has been successfully applied in a wide variety of domains (Arulkumaran et al., 2017). In typical RL scenarios, the RL agent learns a single task, defined as a single Partially Observable Markov Decision Process (POMDP).