Continual Reinforcement Learning with TELLA
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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.
Key facts. Cited by 1 paper 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
14 of 16 references.
References · 14
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2016OpenAI Gymcited by 1,736
- 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
- 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,204
- 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
- 2018Progress & Compress: A scalable framework for continual learningcited by 520
- 2017A Brief Survey of Deep Reinforcement Learningcited by 483
- 2019Leveraging Procedural Generation to Benchmark Reinforcement Learningcited by 483
- 2020Towards Continual Reinforcement Learning: A Review and Perspectivescited by 274
- 2019Measuring the Reliability of Reinforcement Learning Algorithmscited by 51
- 2021CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agentscited by 38
- 2022Lifelong Learning Metricscited by 13
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Citing papers per year: 2022: 1.
1 paper.