A Survey of Continual Reinforcement Learning

1. 1Identify the limitations of traditional RL in generalizing across tasks
2. 2Explore CRL methods for efficient learning and adaptation in changing environments
3. 3Investigate the role of deep neural networks in CRL
4. 4Develop strategies for balancing exploration and exploitation in CRL

AI engineers and ML researchers benefit from understanding CRL to improve model generalization and efficiency in dynamic environments

💡 CRL aims to improve RL's ability to generalize across tasks and adapt to changing environments

💡 Continual Reinforcement Learning (CRL) tackles sequential decision-making challenges in dynamic environments