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Introduces CCPL, a method to address delayed and stochastic consequences in constrained reinforcement learning using a delay-corrected Bellman operator and an Interventional Consequence Net for causal attribution, with a contraction proof under unknown delays.
This paper introduces a novel preference-conditioned Bellman operator based on Chebyshev scalarization to compute deterministic Pareto-optimal policies for Multi-Objective Markov Decision Processes, proving its convergence and effectiveness in capturing the entire Pareto frontier.
This paper introduces Path-Coupled Bellman Flows (PCBF), a continuous-time distributional reinforcement learning method that uses flow matching to model return distributions without heuristic projections. It addresses boundary mismatch and high-variance issues in previous flow-based approaches by coupling current and successor return flows through shared base noise.