Constrained Deep Reinforcement Learning

Constrained Deep Reinforcement Learning (C-DRL) focuses on training AI agents to make optimal decisions while adhering to real-world limitations or constraints. Current research emphasizes applying C-DRL to diverse domains, including autonomous driving, communication network optimization, and energy management, often employing algorithms like soft actor-critic and primal-dual deep deterministic policy gradient methods within tailored architectures. This approach is significant because it enables the development of robust and safe AI systems capable of handling complex, constrained environments, leading to improvements in various applications from safer vehicles to more efficient energy grids.