  Safe offline RL is a promising way to bypass risky online interactions
towards safe policy learning. Most existing methods only enforce soft
constraints, i.e., constraining safety violations in expectation below
thresholds predetermined. This can lead to potentially unsafe outcomes, thus
unacceptable in safety-critical scenarios. An alternative is to enforce the
hard constraint of zero violation. However, this can be challenging in offline
setting, as it needs to strike the right balance among three highly intricate
and correlated aspects: safety constraint satisfaction, reward maximization,
and behavior regularization imposed by offline datasets. Interestingly, we
discover that via reachability analysis of safe-control theory, the hard safety
constraint can be equivalently translated to identifying the largest feasible
region given the offline dataset. This seamlessly converts the original trilogy
problem to a feasibility-dependent objective, i.e., maximizing reward value
within the feasible region while minimizing safety risks in the infeasible
region. Inspired by these, we propose FISOR (FeasIbility-guided Safe Offline
RL), which allows safety constraint adherence, reward maximization, and offline
policy learning to be realized via three decoupled processes, while offering
strong safety performance and stability. In FISOR, the optimal policy for the
translated optimization problem can be derived in a special form of weighted
behavior cloning. Thus, we propose a novel energy-guided diffusion model that
does not require training a complicated time-dependent classifier to extract
the policy, greatly simplifying the training. We compare FISOR against
baselines on DSRL benchmark for safe offline RL. Evaluation results show that
FISOR is the only method that can guarantee safety satisfaction in all tasks,
while achieving top returns in most tasks.
