Continual Reinforcement Learning with TELLA
This addresses the problem of lack of reproducibility and standard metrics in continual reinforcement learning for researchers.
The paper tackles the challenge of training reinforcement learning agents across multiple environments by introducing TELLA, a tool for reproducible curricula and standardized evaluation, which logs detailed data for analysis.
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. To address this, we present TELLA, a tool for the Test and Evaluation of Lifelong Learning Agents. TELLA provides specified, reproducible curricula to lifelong learning agents while logging detailed data for evaluation and standardized analysis. 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.