  Offline safe reinforcement learning (RL) aims to train a policy that
satisfies constraints using a pre-collected dataset. Most current methods
struggle with the mismatch between imperfect demonstrations and the desired
safe and rewarding performance. In this paper, we introduce OASIS (cOnditionAl
diStributIon Shaping), a new paradigm in offline safe RL designed to overcome
these critical limitations. OASIS utilizes a conditional diffusion model to
synthesize offline datasets, thus shaping the data distribution toward a
beneficial target domain. Our approach makes compliance with safety constraints
through effective data utilization and regularization techniques to benefit
offline safe RL training. Comprehensive evaluations on public benchmarks and
varying datasets showcase OASIS's superiority in benefiting offline safe RL
agents to achieve high-reward behavior while satisfying the safety constraints,
outperforming established baselines. Furthermore, OASIS exhibits high data
efficiency and robustness, making it suitable for real-world applications,
particularly in tasks where safety is imperative and high-quality
demonstrations are scarce.
