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Found 12 papers, 1 papers with code
Optimizing Expectation with Guarantees in POMDPs (Technical Report)
In this work we go beyond both the "expectation" and "threshold" approaches and consider a "guaranteed payoff optimization (GPO)" problem for POMDPs, where we are given a threshold $t$ and the objective is to find a policy $\sigma$ such that a) each possible outcome of $\sigma$ yields a discounted-sum payoff of at least $t$, and b) the expected discounted-sum payoff of $\sigma$ is optimal (or near-optimal) among all policies satisfying a).
Threshold Constraints with Guarantees for Parity Objectives in Markov Decision Processes
We establish that, for all variants of this problem, deciding the existence of a strategy lies in ${\sf NP} \cap {\sf coNP}$, the same complexity class as classical parity games.
Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraints
Assuming the support of the unknown transition function and a lower bound on the minimal transition probability are known in advance, we show that in MDPs consisting of a single end component, two combinations of guarantees on the parity and mean-payoff objectives can be achieved depending on how much memory one is willing to use.
Learning Non-Markovian Reward Models in MDPs
There are situations in which an agent should receive rewards only after having accomplished a series of previous tasks.
Mixing Probabilistic and non-Probabilistic Objectives in Markov Decision Processes
In this paper, we consider algorithms to decide the existence of strategies in MDPs for Boolean combinations of objectives.
Safe Learning for Near Optimal Scheduling
In this paper, we investigate the combination of synthesis, model-based learning, and online sampling techniques to obtain safe and near-optimal schedulers for a preemptible task scheduling problem.
Online Learning of Non-Markovian Reward Models
In our formal setting, we consider a Markov decision process (MDP) that models the dynamics of the environment in which the agent evolves and a Mealy machine synchronized with this MDP to formalize the non-Markovian reward function.
Stackelberg-Pareto Synthesis (Full Version)
In this paper, we study the framework of two-player Stackelberg games played on graphs in which Player 0 announces a strategy and Player 1 responds rationally with a strategy that is an optimal response.
Computer Science and Game Theory
Active Learning of Sequential Transducers with Side Information about the Domain
We show that there exists an algorithm using string equation solvers that uses this knowledge to learn subsequential string transducers with a better guarantee on the required number of equivalence queries than classical active learning.
Lifted Model Checking for Relational MDPs
We present pCTL-REBEL, a lifted model checking approach for verifying pCTL properties of relational MDPs.
Model-based Reinforcement Learning
reinforcement-learning
+1
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains
The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert.
Formally-Sharp DAgger for MCTS: Lower-Latency Monte Carlo Tree Search using Data Aggregation with Formal Methods
In particular, we use model-checking techniques to guide the MCTS algorithm in order to generate offline samples of high-quality decisions on a representative set of states of the MDP.
