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Learning Constrained Markov Decision Processes With Non-stationary Rewards and Constraints
In constrained Markov decision processes (CMDPs) with adversarial rewards and constraints, a well-known impossibility result prevents any algorithm from attaining both sublinear regret and sublinear constraint violation, when competing against a best-in-hindsight policy that satisfies constraints on average.
Learning Adversarial MDPs with Stochastic Hard Constraints
To the best of our knowledge, our work is the first to study CMDPs involving both adversarial losses and hard constraints.
Markov Persuasion Processes: Learning to Persuade from Scratch
Recently, Markov persuasion processes (MPPs) have been introduced to capture sequential scenarios where a sender faces a stream of myopic receivers in a Markovian environment.
A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints
We study online learning in episodic constrained Markov decision processes (CMDPs), where the goal of the learner is to collect as much reward as possible over the episodes, while guaranteeing that some long-term constraints are satisfied during the learning process.
