The field of reinforcement learning is moving towards continual learning, enabling agents to learn continuously, adapt to new tasks, and retain previously acquired knowledge. This shift is driven by the need to address the limitations of traditional reinforcement learning, such as the requirement for extensive training data and computational resources, and the limited ability to generalize across tasks. Researchers are exploring new methodologies, including the development of novel taxonomy and the use of domain-specific languages. Notable developments include the creation of benchmarking suites for real-world reinforcement learning and the introduction of new algorithms that can infer latent task structure without relying on immediate incentives. Some particularly noteworthy papers include:

• A Survey of Continual Reinforcement Learning, which provides a comprehensive examination of continual reinforcement learning, its core concepts, challenges, and methodologies.
• A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control, which proposes a new algorithm that balances memory by gradually reducing the influence of early experiences and enhances agents' capability to exploit patterns of existing data by dynamically adding new parameters during training.
• Ludax: A GPU-Accelerated Domain Specific Language for Board Games, which presents a domain-specific language for board games that automatically compiles into hardware-accelerated code, enabling rapid simulation and providing a flexible representation scheme.