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Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning
Abstract
A key challenge in lifelong reinforcement learning (RL) is the loss of plasticity, where previous learning progress hinders an agent's adaptation to new tasks. While regularization and resetting can help, they require precise hyperparameter selection at the outset and environment-dependent adjustments. Building on the principled theory of online convex optimization, we present a parameter-free optimizer for lifelong RL, called TRAC, which requires no tuning or prior knowledge about the distribution shifts. Extensive experiments on Procgen, Atari, and Gym Control environments show that TRAC works surprisingly well-mitigating loss of plasticity and rapidly adapting to challenging distribution shifts-despite the underlying optimization problem being nonconvex and nonstationary.
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Cited by
Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning
Wu, Z., Lou, X., Ma, X., Li, Y., Liu, W., Zhang, W., Wang, J., & Zhang, Z. (2026) · arXiv · doi:10.48550/arxiv.2601.03641
“Existing approaches primarily tackle this via two paradigms: incorporating external memory modules (Ouyang et al., 2025; Zhang et al., 2025b) or conducting continuous iterative training (Zhang et al., 2025d; Muppidi et al., 2024).”
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Metadata
- Authors
- Muppidi, A., Zhang, Z., & Yang, H.
- ORCID
- Yang, Heng
- Venue
- Advances in Neural Information Processing Systems 37
- Published
- 2024
- Pages
- 51169-51195
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W4415797711
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- parameter-free optimization, lifelong reinforcement learning, meta-optimization
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
