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Found 6 papers, 0 papers with code
Learning an Inventory Control Policy with General Inventory Arrival Dynamics
In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arrivals model (QOT).
Contextual Bandits for Evaluating and Improving Inventory Control Policies
Solutions to address the periodic review inventory control problem with nonstationary random demand, lost sales, and stochastic vendor lead times typically involve making strong assumptions on the dynamics for either approximation or simulation, and applying methods such as optimization, dynamic programming, or reinforcement learning.
Linear Reinforcement Learning with Ball Structure Action Space
We study the problem of Reinforcement Learning (RL) with linear function approximation, i. e. assuming the optimal action-value function is linear in a known $d$-dimensional feature mapping.
Learning in structured MDPs with convex cost functions: Improved regret bounds for inventory management
We consider the relatively less studied problem of designing a learning algorithm for this problem when the underlying demand distribution is unknown.
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
Our main result is a high probability regret upper bound of $\tilde{O}(D\sqrt{SAT})$ for any communicating MDP with $S$ states, $A$ actions and diameter $D$, when $T\ge S^5A$.
Posterior sampling for reinforcement learning: worst-case regret bounds
We present an algorithm based on posterior sampling (aka Thompson sampling) that achieves near-optimal worst-case regret bounds when the underlying Markov Decision Process (MDP) is communicating with a finite, though unknown, diameter.
