Stars
These Python scripts read SUMO traffic simulation results from the FCD (floating car data) export in the CSV format and convert it to the time-dynamic visualization format CZML.
Computational framework for reinforcement learning in traffic control
Eclipse SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians a…
Automated traffic scenario generation in CommonRoad.
Route and Reference Path Planning Toolbox
A collection and interface for CommonRoad prediction algorithms.
Toolbox for Map Conversion and Scenario Creation for Autonomous Vehicles.
Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
Adaptive Risk Tendency Implicit Quantile Network for Drone Navigation under Partial Observability.
MineRL Competition for Sample Efficient Reinforcement Learning - Python Package
Bao, a Lightweight Static Partitioning Hypervisor
Implementation of Tactical Optimistic and Pessimistic value estimation
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Code for "Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans" https://arxiv.org/abs/2001.00735
This model leverages attention mechanisms and inverse reinforcement learning (IRL) to predict vehicle trajectories in dynamic environments. The attention mechanism enables the model to focus on key…
Official implementation of "Regularizing neural networks for future trajectory prediction via IRL framework" published in IET CV
This python package contains scripts needed to train IRL Driver models on HighD datasets. This code is accompanying the paper "Validating human driver models for interaction-aware automated vehicle…
Tactics2D: A Reinforcement Learning Environment Library with Generative Scenarios for Driving Decision-making
[T-ITS] Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning
Implementation of Inverse Reinforcement Learning (IRL) algorithms in Python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
PyTorch implementation of Maximum Entropy Deep Inverse Reinforcement Learning
Code for running the implementations proposed in: Westny, T., Oskarsson, J., Olofsson, B. and Frisk, E., "MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs", IEEE…
Official implementation of the Dronalize toolbox for trajectory prediction research.
Open source code combining implementations of Upside Down Reinforcement Learning and Reward Conditioned Policies