reinforcement learning

From Optimization to Control: Quasi Policy Iteration

  • Mohamad Amin Sharifi Kolarijani
  • Peyman Mohajerin Esfahani
    • Categories Convex Optimization, Dynamic Programming Tags , , , ,

    Recent control algorithms for Markov decision processes (MDPs) have been designed using an implicit analogy with well-established optimization algorithms. In this paper, we make this analogy explicit across four problem classes with a unified solution characterization. This novel framework, in turn, allows for a systematic transformation of algorithms from one domain to the other. In … Read more

    Dynamic courier capacity acquisition in rapid delivery systems: a deep Q-learning approach

  • Ramon Auad
  • Alan L. Erera
  • Martin Savelsbergh
    • Categories Applications - OR and Management Sciences, Production and Logistics, Transportation Tags , , , , ,

    With the recent boom of the gig economy, urban delivery systems have experienced substantial demand growth. In such systems, orders are delivered to customers from local distribution points respecting a delivery time promise. An important example is a restaurant meal delivery system, where delivery times are expected to be minutes after an order is placed. … Read more

    Batch Learning in Stochastic Dual Dynamic Programming

  • Daniel Ávila
  • Nils Löhndorf
  • Anthony Papavasiliou
    • Categories Dynamic Programming, Stochastic Programming Tags , , , ,

    We consider the stochastic dual dynamic programming (SDDP) algorithm, which is a widely employed algorithm applied to multistage stochastic programming, and propose a variant using batch learning, a technique used with success in the reinforcement learning framework. We cast SDDP as a type of Q-learning algorithm and describe its application in both risk neutral and … Read more

    An Adaptive and Near Parameter-free BRKGA Using Q-Learning Method

  • Antonio Chaves
  • Luiz Henrique Lorena
    • Categories Meta Heuristics Tags , , ,

    The Biased Random-Key Genetic Algorithm (BRKGA) is an efficient metaheuristic to solve combinatorial optimization problems but requires parameter tuning so the intensification and diversification of the algorithm work in a balanced way. There is, however, not only one optimal parameter configuration, and the best configuration may differ according to the stages of the evolutionary process. … Read more

    SDP-based bounds for the Quadratic Cycle Cover Problem via cutting plane augmented Lagrangian methods and reinforcement learning

  • Frank de Meijer
  • Renata Sotirov
    • Categories Combinatorial Optimization, Semi-definite Programming Tags , , , , ,

    We study the Quadratic Cycle Cover Problem (QCCP), which aims to find a node-disjoint cycle cover in a directed graph with minimum interaction cost between successive arcs. We derive several semidefinite programming (SDP) relaxations and use facial reduction to make these strictly feasible. We investigate a nontrivial relationship between the transformation matrix used in the … Read more

    Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning

  • Yichen Chen
  • Mengdi Wang
    • Categories Dynamic Programming Tags ,

    We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minimax duality of Bellman equations. The SPD methods update a few coordinates of the value and policy estimates as a new state transition is observed. These methods … Read more