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cityflow

A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

cooperationinsocialnetworks

Achieving Global Cooperation in Social Networks Through Peer Punishment

deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

deviationsfromprofile

Deviations from Profiles in UK Motorways - Thales, Highways UK and University of Warwick

freshflow_takehome

gym

A toolkit for developing and comparing reinforcement learning algorithms.

ieee-profiles

Published in IEEE ITSC2018. Paper on generation of Traffic Profiles with Spectral Analysis and Non-Parametric Regression.

ieee_itsc2020_warp

Presented in IEEE ITSC2020 (Sep'20) Pre-print version and code. WARP: Wavelet Augmented Regression Profiling

ieee_smc2020_reward_functions_rl_utc

Presented in IEEE 23rd International Conference in Systems, Man, and Cybernetics. Pre-print version of "Assessment of Reward Functions for Reinforcement Learning Vehicular Traffic Signal Control Under Real-World Limitations"

ieee_smc2020_rl_urbantraffic_multimodal

Pre-print version of "Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations"

ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

noisyoptimisationwithstochasticannealing

Optimisation of computationally intensive noisy simulations with Optimal Stochastic Annealing

ntis_profiles

Travel Time Profile Estimations using seasonality extraction via non-parametric regression models applied to UK NTIS Motorway data

paperrewardfunctionsintrafficrl

Comparison between Reward Functions for Urban Traffic Control RL in a real-world intersection.

pythondatasciencehandbook

Jupyter Notebooks for the Python Data Science Handbook

rl_interview

timescales_and_discount_factor_in_utc

Looking into the relevance of \gamma (discount factor) in RL applied to Urban Traffic Control, looking at the relationship between it and how the timescales in which the state variables vary

vissim_rl_traffic_lights_control

VISSIM Reinforcement Learning for Urban Traffic Control. DQN, DDQN, D3QN, A2C, PER.