State Action Pair

State-action pairs represent the fundamental units of interaction in reinforcement learning (RL), encompassing the agent's state and chosen action at a given time step. Current research focuses on improving the representation and utilization of state-action pairs, particularly through advanced model architectures like diffusion models and ensemble methods, and algorithms such as those employing bisimulation or contrastive learning to enhance sample efficiency and robustness. This work is crucial for advancing RL's capabilities in complex environments, particularly in offline settings and safety-critical applications, by enabling more accurate value function estimation, improved policy learning, and better generalization.