Unifying recent RL and IL algorithms under a single framework

Determine whether a unified framework can be established to inspect a variety of recent off-policy reinforcement learning and imitation learning algorithms so as to understand their limitations and subsequently enable better methods.

Background

The paper notes that many recent off-policy RL and IL methods were derived using diverse mathematical tools and motivated by differing perspectives, which makes their relationships and shared limitations hard to discern. Establishing a single unifying viewpoint would clarify common structures and failure modes and could guide the design of improved algorithms.

This question motivates the work’s dual RL perspective and unification results, but the sentence explicitly frames the need for such a unified framework as an open question.

References

It remains an open question if we can inspect these algorithms under a unified framework to understand their limitations, and subsequently propose better methods.

— Dual RL: Unification and New Methods for Reinforcement and Imitation Learning  (2302.08560 - Sikchi et al., 2023) in Section 1 (Introduction)

An open question is how action decision-making, world simulation, and value estimation can be coupled within a unified model to exploit the complementary supervision provided by heterogeneous interaction data---including expert demonstrations, successful trajectories, suboptimal executions, and failures---for policy, dynamics, and value learning.

— Motus2: A Self-Evolving General World Model for Dexterous Manipulation  (2608.30237 - Bi et al., 31 Aug 2026) in Section 2, Related Work, paragraph “World-Model-Based Policy Improvement”