# Continual Reinforcement Learning with TELLA

arXiv: https://arxiv.org/abs/2208.04287 · HTML: https://mlsanity.com/paper/2208.04287/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 16 references that are in the map.
- First posted on arXiv 8 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Leveraging Procedural Generation to Benchmark Reinforcement Learning](https://mlsanity.com/paper/1912.01588/) (2019)
- [Towards Continual Reinforcement Learning: A Review and Perspectives](https://mlsanity.com/paper/2012.13490/) (2020)
- [Measuring the Reliability of Reinforcement Learning Algorithms](https://mlsanity.com/paper/1912.05663/) (2019)
- [CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents](https://mlsanity.com/paper/2110.10067/) (2021)
- [Lifelong Learning Metrics](https://mlsanity.com/paper/2201.08278/) (2022)
- [ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives](https://mlsanity.com/paper/2112.04123/) (2021)
- [Avalanche RL: a Continual Reinforcement Learning Library](https://mlsanity.com/paper/2202.13657/) (2022)

## Built on by (most cited first, 1 of 1)

- [System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games](https://mlsanity.com/paper/2212.04603/) (2022)

## Abstract (arXiv)

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.