gszmnsk's Stars
dotchen/LAV
(CVPR 2022) A minimalist, mapless, end-to-end self-driving stack for joint perception, prediction, planning and control.
dotchen/LearningByCheating
(CoRL 2019) Driving in CARLA using waypoint prediction and two-stage imitation learning
praveen-palanisamy/macad-gym
Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:
woven-planet/l5kit
L5Kit - https://woven.toyota
johnMinelli/carla-gym
Multi-agent reinforcement learning interface for CARLA Autonomous Driving simulator compatible with PettingZoo
mit-wu-lab/automatic_vehicular_control
[IEEE T-ASE] [IROS 2022] Unified Automatic Control of Vehicular Systems With Reinforcement Learning
Pi-Star-Lab/RESCO
Reinforcement Learning Benchmarks for Traffic Signal Control (RESCO)
LucasAlegre/sumo-rl
Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.
panmt/MBRL_with_Isolated_Imaginations
Plankson/CSIRL
Curricular Subgoal for Inverse Reinforcement Learning
liangchunyaobing/RCM-AIRL
public code and data
zyh1999/CADP
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
starry-sky6688/MARL-Algorithms
Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
rafaelmp2/causal-marl
prameshk/Traffic-Assignment
This program solves the user equilibrium and stochastic user equilibrium for the city network
ZhengLi95/User-Equilibrium-Solution
Program for obtaining the user equilibrium solution with Frank-Wolfe Algorithm in urban traffic assignment
munirjojoverge/rl_AD_urban_baselines
Reinforcement Learning Baselines (OpenAI) applied to Autonomous Driving
bstabler/TransportationNetworks
Transportation Networks for Research
matteobettini/Traffic-Assignment-Frank-Wolfe-2021
This simple script computes the traffic assignment using the Frank-Wolfe algorithm (FW) or the Method of successive averages (MSA). It can compute the User Equilibrium (UE) assignment or the System Optimal (SO) assignment. The travel time cost function that models the effect of congestion on travel time is pluggable and definable by the users.
kmisztal/effective_python
Effective Python programming (in Polish, maybe in English in near future)
guroosh/CS7IS2-AI-project
Optimal Path Finding in a GridWorld using A* algorithm, Genetic Algorithm and Reinforcement Learning (Q-Learning and SARSA)
shiluyuan/Reinforcement-Learning-in-Path-Finding
Reinforce Learing, Q-Rounting, Shortest-Path
DmitryUlyanov/Multicore-TSNE
Parallel t-SNE implementation with Python and Torch wrappers.
Andras7/word2vec-pytorch
Extremely simple and fast word2vec implementation with Negative Sampling + Sub-sampling
udacity/deep-learning-v2-pytorch
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
AlexandreH13/word_similarity_visualization
Word similarity using Word2Vec model and Networkx
OlgaChernytska/word2vec-pytorch
Implementation of the first paper on word2vec
ikostrikov/rlpd
AI4Finance-Foundation/FinRL
FinRL: Financial Reinforcement Learning. 🔥
AI4Finance-Foundation/ElegantRL
Massively Parallel Deep Reinforcement Learning. 🔥