  The robust constrained Markov decision process (RCMDP) is a recent
task-modelling framework for reinforcement learning that incorporates
behavioural constraints and that provides robustness to errors in the
transition dynamics model through the use of an uncertainty set. Simulating
RCMDPs requires computing the worst-case dynamics based on value estimates for
each state, an approach which has previously been used in the Robust
Constrained Policy Gradient (RCPG). Highlighting potential downsides of RCPG
such as not robustifying the full constrained objective and the lack of
incremental learning, this paper introduces two algorithms, called RCPG with
Robust Lagrangian and Adversarial RCPG. RCPG with Robust Lagrangian modifies
RCPG by taking the worst-case dynamics based on the Lagrangian rather than
either the value or the constraint. Adversarial RCPG also formulates the
worst-case dynamics based on the Lagrangian but learns this directly and
incrementally as an adversarial policy through gradient descent rather than
indirectly and abruptly through constrained optimisation on a sorted value
list. A theoretical analysis first derives the Lagrangian policy gradient for
the policy optimisation of both proposed algorithms and then the adversarial
policy gradient to learn the adversary for Adversarial RCPG. Empirical
experiments injecting perturbations in inventory management and safe navigation
tasks demonstrate the competitive performance of both algorithms compared to
traditional RCPG variants as well as non-robust and non-constrained ablations.
In particular, Adversarial RCPG ranks among the top two performing algorithms
on all tests.

Machine Learning;Artificial Intelligence;Neural and Evolutionary Computing