RLRSRL

RLRSRL is an acronym encountered in several research works to denote a class of methods that combines reinforcement learning with regularized representation learning for sequential decision problems. Because there is no single standard expansion of the acronym, the exact meaning of RLRSRL varies by source. In many papers, RLRSRL refers to approaches where a policy is learned through a reinforcement-learning objective while a representation module learns compact state and action representations under a regularization penalty designed to promote sparsity, disentanglement, or invariance.

Common elements often associated with RLRSRL include:

- A reinforcement learning objective that drives policy improvement to maximize cumulative reward.

- A representation learning component that extracts latent state and action features.

- Regularization mechanisms, such as L1/L2 penalties, entropy bonuses, or mutual information terms, to constrain representations.

- Joint or alternating optimization of the policy and representation modules.

- Variants spanning model-free and model-based approaches, with attention to data efficiency and generalization.

- Applications aimed at improved sample efficiency, transfer learning, or robustness to domain shifts.

Relation to broader fields:

RLRSRL sits at the intersection of reinforcement learning, deep learning, and representation learning. It is used in contexts ranging from robotics and control to simulated environments and game AI, with the goal of producing more data-efficient and generalizable agents. The term remains somewhat heterogeneous in usage, reflecting evolving ideas about how best to couple representation learning with decision-making.

See also: reinforcement learning, deep reinforcement learning, representation learning, regularization, sparse coding.