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A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm
Code of Paper "Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach", JSA 2022.
A real-world dataset for EV-related research, e.g., spatiotemporal prediction and urban energy management.
A generic evolutionary game theory simulation library
“Population-based Cooperative Gaming for Unsupervised Person Re-identification”, IJCV 2023.
Using improved PSO(Particle Swarm Optimization) algorithm resolve VRPTW question.
This project implements Q-Learning to find the optimal policy for charging and discharging electric vehicles in a V2G scheme under conditions of uncertain commitment of EV owners. The problem is mo…
Implementing Algorithms for Computing Stackelberg Equilibria in Security Games
A vehicular network simulator (SUMO+ns-3) encapsulated behind Gym API (OpenAI-Gym), which allows users to evaluate RL-enhanced vehicular network. Furthermore, the V2X-Gym accepts XML-format files a…
A Simulator for Dynamic Ride-Sharing with Pooling: Joint Matching,Pricing, Route Planning, and Dispatching
Distributed EV Charge Scheduling Algorithms
The three algorithms used to solve Bayesian Stackelberg Games have been implemented here.
The python code generated random demands of random EV vehicles and household electricity demands. It then plots the graphs between earlier energy peaks and reductions in peaks after the implementat…
This is code for finding the minimax/nash/stackelberg strategy of players in Markov Games.
Game Theory Based Distributed Resource Allocation in Communication Networks
The python codes implement the EV charging problem as static and dynamic optimization problem. The optimizers try to maximize the revenue and minimize the cost of the EV charging plant.
Package wrote python to solve dynamic Stackelberg game with constraints. Based on ECC 2019 accepted paper titled "Linear quadratic Stackelberg difference games with constraints".
This is the numerical approach proposed in the paper "Optimal Incentives to Mitigate Epidemics: A Stackelberg Mean Field Game Approach" by A. Aurell, R. Carmona, G. Dayanikli, M. Lauriere.
The repository mainly includes the codes of the multi-leader multi-follower game. The game consists of two layers, i.e., buyer operators as followes, and seller operators as leaders.
Learning-Based Optimal Charging Discharging Strategy for Electric Vehicles Under Vehicle-to-Grid Scheme
Implements an Multi Leader Follower (L/F) model and solves for L/F Nash Equilibrium
Python implementation of Maximizing Matching in Double-sided Auctions algorithm
The effects of proof-of-stake design on consensus: a game-theoretical simulation-based approach