Continuous Action

Continuous action reinforcement learning focuses on developing algorithms that enable agents to learn optimal policies in environments where actions are continuous rather than discrete. Current research emphasizes efficient exploration strategies within these high-dimensional action spaces, employing techniques like model-based methods, actor-critic architectures (including Wasserstein-based approaches), and novel policy optimization algorithms that leverage optimal transport or no-regret learning frameworks. These advancements are crucial for tackling complex real-world problems, such as robotics control, personalized medicine (e.g., optimal dosage determination), and smart city applications, where continuous actions are inherent.