Policy Constraint

Policy constraint in reinforcement learning focuses on ensuring learned agent policies adhere to predefined safety or behavioral limitations, preventing undesirable actions while optimizing for a primary objective. Current research emphasizes developing dynamic and adaptive constraint methods, often integrated into offline reinforcement learning algorithms like TD3-BC and CQL, or employing novel architectures such as decision transformers and conditional sequence models (e.g., SaFormer). These advancements aim to address limitations of static constraints, improve sample efficiency, and enable robust policy learning from diverse or imperfect datasets, with applications ranging from robotics to autonomous systems.