REVIEW 5 cited by
Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Robust Constrained-MDPs: Soft-Constrained Robust Policy Optimization under Model Uncertainty
read the original abstract
In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties. Indeed, in the framework of model-based RL, we propose to merge the theory of constrained Markov decision process (CMDP), with the theory of robust Markov decision process (RMDP), leading to a formulation of robust constrained-MDPs (RCMDP). This formulation, simple in essence, allows us to design RL algorithms that are robust in performance, and provides constraint satisfaction guarantees, with respect to uncertainties in the system's states transition probabilities. The need for RCMPDs is important for real-life applications of RL. For instance, such formulation can play an important role for policy transfer from simulation to real world (Sim2Real) in safety critical applications, which would benefit from performance and safety guarantees which are robust w.r.t model uncertainty. We first propose the general problem formulation under the concept of RCMDP, and then propose a Lagrangian formulation of the optimal problem, leading to a robust-constrained policy gradient RL algorithm. We finally validate this concept on the inventory management problem.
Forward citations
Cited by 5 Pith papers
-
Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees
RHC-UCRL is the first algorithm for safety-constrained RL under explicit adversarial dynamics, providing sub-linear regret and constraint violation guarantees by maintaining optimism over both agent and adversary policies.
-
Characterization and Analysis of Emergency Landing Flight Envelopes with Graded Safety Specifications
Introduces a graded safety framework for emergency landing flight envelopes using state- and time-dependent violation cost functions in Hamilton-Jacobi reachability, with monotonicity properties and a convergent synth...
-
Revisiting Subgradient Dominance in Robust MDPs: Counterexamples, Hardness, and Sufficient Conditions
RMDPs lack subgradient dominance in general and admit suboptimal local minima; finding epsilon-optimal policies is NP-hard for finite transition uncertainty sets, but the dominance property holds when worst-case kerne...
-
Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form
Presents the first algorithm to identify an ε-optimal policy in robust constrained MDPs via epigraph form and bisection search with Õ(ε^{-4}) robust policy evaluations.
-
Robust Shielding for Safe Reinforcement Learning
A sound and optimal shielding method for robust MDPs ensures LTL safety under worst-case transitions and combines with PAC sampling to produce minimally restrictive shields for learned models.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.