kyutian's Stars
hsvgbkhgbv/SQDDPG
This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.
TonghanWang/NDQ
Codes accompanying the paper "Learning Nearly Decomposable Value Functions with Communication Minimization" (ICLR 2020)
011235813/cm3
Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
iRB-Lab/py-ga-VRPTW
A Python Implementation of a Genetic Algorithm-based Solution to Vehicle Routing Problem with Time Windows
Starofall/CrowdNav
A model problem for big data self adaptive systems using SUMO and TraCI
anita-hu/TF2-RL
Reinforcement learning algorithms implemented for Tensorflow 2.0+ [DQN, DDPG, AE-DDPG, SAC, PPO, Primal-Dual DDPG]
iliasger/TRAPP
TRAPP framework for Traffic Reconfiguration via Adaptive Participatory Planning
bstriner/gym-traffic
OpenAI Gym Environment for Traffic Control
lcodeca/LuSTScenario
Luxembourg SUMO Traffic (LuST) Scenario
oxwhirl/pymarl
Python Multi-Agent Reinforcement Learning framework
lcodeca/PyPML
Python Parking Monitoring Library for SUMO
mickedav/RouteFlowEstimation
Modelling traffic flow using Google Directions API
streettraffic/streettraffic
StreetTraffic is a Python package that crawls the traffic flow data of your favorite routes, cities by using the API provided by HERE.com
sraddon/SUMO-V2X-Communication-Research-Platooning-and-CIM
Traffic simulation project for dissertation
xieming11lvyou/sumo-reroute
a-b-street/abstreet
Transportation planning and traffic simulation software for creating cities friendlier to walking, biking, and public transit
gboeing/osmnx-examples
Gallery of OSMnx tutorials, usage examples, and feature demonstations.
Rafael1s/Deep-Reinforcement-Learning-Algorithms
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
keras-rl/keras-rl
Deep Reinforcement Learning for Keras.
google/dopamine
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
cyrilhokage/RL-Traffic-optimization_CIL4sys
An implementation of the traffic simulation optimisation with reinforcement learning, with FLOW and SUMO.
flow-project/flow
Computational framework for reinforcement learning in traffic control
SugiharaLab/pyEDM
Python package of EDM tools
HimpeWillem/MatlabTrafficToolbox
This is the matlab package for dynamic traffic assignments developed by the KULeuven
OptMLGroup/VRP-RL
Reinforcement Learning for Solving the Vehicle Routing Problem
margaritas98/Intelligent-Transport-Services
A SUMO Simulation of Urban MObility model of interrupted traffic re-routing framework
Universite-Gustave-Eiffel/SymuRes
A Multi-Reservoir MFD-Based Traffic Simulation Platform
HimpeWillem/OpenTrafficCenter
A basic implementation of a traffic monitoring and control center of the Flemish Highway system in Matlab.
SudiptaSingh/Q-Learning-based-smart-cab
Problem Statement A smart city needs smart mobility, and to achieve this objective, the travel should be made convenient through sustainable transport solutions. Transportation system all over the world is facing unprecedented challenges in the current scenario of increased population, urbanization and motorization. Farewell to all difficulties as reinforcement learning along with deep learning can now make it simpler for consumers. In this paper we have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time. We have first investigated the environment, the agent operates in, by constructing a very basic driving implementation. Once the agent is successful at operating within the environment, we can then identify each possible state the agent can be in when considering such things as traffic lights and oncoming traffic at each intersection. With states identified, we can implement a Q-Learning algorithm for the self-driving agent to guide the agent towards its destination within the allotted time. Finally, we can improve upon the Q-Learning algorithm to find the best configuration of learning and exploration factors to ensure the self-driving agent is reaching its destinations with consistently positive results. Our aim is also to find optimum values of parameters of the fitting function alpha, gamma and epsilon, so that the agent can work in an optimized way with the most optimum parameter values. Hence, a comparative analysis has also been conducted. Methodology used The solution to the smart cab objective is deep reinforcement learning in a simulated environment. The smart cab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. We have assumed that the smart cab is assigned a route plan based on the passengers' starting location and destination. The route is split at each intersection into waypoints, and the smart cab, at any instant, is at some intersection in the world. Therefore, the next waypoint to the destination, assuming the destination has not already been reached, is one intersection away in one direction (North, South, East, or West). The smart cab has only an egocentric view of the intersection it is at: It can determine the state of the traffic light for its direction of movement, and whether there is a vehicle at the intersection for each of the oncoming directions. For each action, the smart cab may either stay idle at the intersection, or drive to the next intersection to the left, right, or ahead of it. Finally, each trip has a time to reach the destination which decreases for each action taken (the passengers want to get there quickly). If the allotted time becomes zero before reaching the destination, the trip has failed. The smart cab will receive positive or negative rewards based on the action it has taken. Expectedly, the smart cab will receive a small positive reward when making a good action, and a varying amount of negative reward dependent on the severity of the traffic violation it would have committed. Based on the rewards and penalties the smart cab receives, the self-driving agent implementation should learn an optimal policy for driving on the city roads while obeying traffic rules, avoiding accidents, and reaching passengers' destinations in the allotted time. Environment: The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply: On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection. On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left.
rtqichen/torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.