The field of reinforcement learning is advancing rapidly, with a focus on developing more efficient and robust methods for control and optimization. Recent research has explored the use of multi-fidelity frameworks, hierarchical architectures, and continual learning to improve the performance of reinforcement learning algorithms. These advances have significant implications for a range of applications, including engineering design optimization, autonomous robot navigation, and building management. Noteworthy papers in this area include:

• Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization, which proposes a novel framework for reducing variance in policy learning.
• Continual Reinforcement Learning for HVAC Systems Control, which introduces a model-based reinforcement learning framework that uses a Hypernetwork to continuously learn environment dynamics.
• Extendable Long-Horizon Planning via Hierarchical Multiscale Diffusion, which proposes a novel approach for extendable long-horizon planning using a hierarchical multiscale diffuser.
• The Crucial Role of Problem Formulation in Real-World Reinforcement Learning, which highlights the importance of careful problem formulation in reinforcement learning for industrial cyber-physical systems.