  We consider the offline constrained reinforcement learning (RL) problem, in
which the agent aims to compute a policy that maximizes expected return while
satisfying given cost constraints, learning only from a pre-collected dataset.
This problem setting is appealing in many real-world scenarios, where direct
interaction with the environment is costly or risky, and where the resulting
policy should comply with safety constraints. However, it is challenging to
compute a policy that guarantees satisfying the cost constraints in the offline
RL setting, since the off-policy evaluation inherently has an estimation error.
In this paper, we present an offline constrained RL algorithm that optimizes
the policy in the space of the stationary distribution. Our algorithm,
COptiDICE, directly estimates the stationary distribution corrections of the
optimal policy with respect to returns, while constraining the cost upper
bound, with the goal of yielding a cost-conservative policy for actual
constraint satisfaction. Experimental results show that COptiDICE attains
better policies in terms of constraint satisfaction and return-maximization,
outperforming baseline algorithms.
