  Recent advancements in offline reinforcement learning (RL) have underscored
the capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm
that learns the action distribution based on target returns for each state in a
supervised manner. However, prevailing RCSL methods largely focus on
deterministic trajectory modeling, disregarding stochastic state transitions
and the diversity of future trajectory distributions. A fundamental challenge
arises from the inconsistency between the sampled returns within individual
trajectories and the expected returns across multiple trajectories.
Fortunately, value-based methods offer a solution by leveraging a value
function to approximate the expected returns, thereby addressing the
inconsistency effectively. Building upon these insights, we propose a novel
approach, termed the Critic-Guided Decision Transformer (CGDT), which combines
the predictability of long-term returns from value-based methods with the
trajectory modeling capability of the Decision Transformer. By incorporating a
learned value function, known as the critic, CGDT ensures a direct alignment
between the specified target returns and the expected returns of actions. This
integration bridges the gap between the deterministic nature of RCSL and the
probabilistic characteristics of value-based methods. Empirical evaluations on
stochastic environments and D4RL benchmark datasets demonstrate the superiority
of CGDT over traditional RCSL methods. These results highlight the potential of
CGDT to advance the state of the art in offline RL and extend the applicability
of RCSL to a wide range of RL tasks.
