  This study focuses on the topic of offline preference-based reinforcement
learning (PbRL), a variant of conventional reinforcement learning that
dispenses with the need for online interaction or specification of reward
functions. Instead, the agent is provided with fixed offline trajectories and
human preferences between pairs of trajectories to extract the dynamics and
task information, respectively. Since the dynamics and task information are
orthogonal, a naive approach would involve using preference-based reward
learning followed by an off-the-shelf offline RL algorithm. However, this
requires the separate learning of a scalar reward function, which is assumed to
be an information bottleneck of the learning process. To address this issue, we
propose the offline preference-guided policy optimization (OPPO) paradigm,
which models offline trajectories and preferences in a one-step process,
eliminating the need for separately learning a reward function. OPPO achieves
this by introducing an offline hindsight information matching objective for
optimizing a contextual policy and a preference modeling objective for finding
the optimal context. OPPO further integrates a well-performing decision policy
by optimizing the two objectives iteratively. Our empirical results demonstrate
that OPPO effectively models offline preferences and outperforms prior
competing baselines, including offline RL algorithms performed over either true
or pseudo reward function specifications. Our code is available on the project
website: https://sites.google.com/view/oppo-icml-2023 .
