Constrained markov decision process cmdp

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Manufacturer - Wholesaler System Study Based on Markov Decision Process

Economic efficiency of trading and manufacturing enterprises is conditioned by a variety of reasons one of them being a proper pricing policy. Besides cost price, sale/demand correlation and marketing tactics many other factors that cannot be fully ...
Čižovs, Jurijs +2 more
core

A linear programming based approach for composite-action Markov decision processes

, 2019
We study a time homogeneous discrete composite-action Markov decision process (CMDP) which needs to make multiple decisions at each state. In this particular Markov decision process, the state variables are divided into two separable sets and a two ...
Xiaohui Yan +3 more
core +1 more source

CVaR-Constrained Optimization for Risk-Aware Dynamic Resource Allocation in 5G RAN Slicing

IEEE Open Journal of the Communications Society
Dynamic resource allocation across heterogeneous network slices in 5G radio access networks requires balancing conflicting quality-of-service (QoS) requirements under time-varying and bursty traffic conditions.
S. Mahalakshmi, S. Markkandan
doaj +1 more source

Slice-Aware and Computationally Efficient Resource Orchestration for Converged mmWave–PON O-RAN: A Reward-Shaped PPO Approach for Joint DBA and PRB Allocation

Telecom
Converging millimetre-wave (mmWave) radio access with passive optical network (PON) fronthaul under the Open RAN (O-RAN) architecture promises unprecedented capacity for beyond-5G and 6G systems.
Nokwanda Shezi, Bakhe Nleya, Beverly Pule +2 more
doaj +1 more source

Editorial: Safety and security of robotic systems: intelligent algorithms. [PDF]

Front Neurorobot, 2023
Wu C.
europepmc +1 more source

A Digital-Twin-Enabled AI-Driven Adaptive Planning Platform for Sustainable and Reliable Manufacturing

Machines
The manufacturing systems face growing demands due to the instability of the market, the demanding sustainability policies, and the high rate of old equipment, but traditional planning structures are mostly fixed and deterministic, leading to the ...
Mingyuan Li, Chun-Ming Yang, Wei Lo, Yi-Wei Kao +3 more
doaj +1 more source

Approximations for constrained Markov decision problems

, 2018
This chapter studies the finite-state approximation of a discrete-time constrained Markov decision process with compact state space, under the discounted and average cost criteria.
Naci Saldi +5 more
core +1 more source

Low-Carbon Urban Freight Optimization: Per-Order Adaptive Mode Mixing with Demonstration-Regularized Constrained Reinforcement Learning

Applied Sciences
Urban last-mile delivery is a rapidly growing source of city-centre emissions, and decarbonizing it without eroding service quality has become imperative for climate goals.
Shukang Zheng +4 more
doaj +1 more source

Learning Feasible Scalarizations in Constrained Markov Decision Processes Using a Stochastic Meta-Policy

International audienceIn many safety-critical decision-making problems, it can be difficult to manually tune the reward function to obtain an optimal policy that behaves satisfactorily.
Ramdane-Cherif, Amar +3 more
core +3 more sources

Markov Decision Process in the Problem of Dynamic Pricing Policy

Markov decision processes (MDP) are widely used in problems whose solutions may be represented by a certain series of actions. A lot of papers demonstrate successful MDP use in model problems, robotic control problems, planning problems, etc. In addition,
Čižovs, Jurijs, Borisovs, Arkādijs
core

constrained markov decision process
deep reinforcement learning
reinforcement learning

resource allocation
proximal policy optimization
autonomous driving

constrained reinforcement learning