Alternatives and similar repositories for SocialForceModel

Users that are interested in SocialForceModel are comparing it to the libraries listed below

• bhanuvikasr / Deep-RL-TORCS
  Autonomous Navigation using Deep Reinforcement Learning
  ☆24Updated 8 years ago
• rohanchandra30 / GraphRQI
  This is the codebase for our ICRA 2020 submission, GraphRQI: Classifying Driver Behaviors Using Graph Spectrums.
  ☆14Updated 6 years ago
• marooncn / RLnotes
  ☆34Updated 5 years ago
• HubFire / Muti-branch-DDPG-CARLA
  A tensorflow implemention of ECCV2018 Paper：CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
  ☆81Updated 6 years ago
• francescofarina / HeadedSocialForceModel
  ☆38Updated 2 years ago
• zfw1226 / active_tracking_rl
  Active visual tracking library based on PyTorch.
  ☆88Updated last year
• yixiao1 / Action-Based-Representation-Learning
  ☆14Updated 3 years ago
• yfzhang / vehicle-motion-forecasting
  A PyTorch-based deep inverse reinforcement learning pipeline for vehicle motion forecasting
  ☆84Updated 7 years ago
• xie9187 / AsDDPG
  Code for the ICRA2018 paper "Learning with training wheels: Speeding up training with a simple controller for Deep Reinforcement Learning…
  ☆38Updated 7 years ago
• xubo92 / robotics-navigation-deep-reinforcement-learning
  Autonomous vehicle learn how to navigate efficiently at crossroad
  ☆16Updated 8 years ago
• DavidCastilloAlvarado / Path-planning-and-Reinforcement-Learning
  We will create a map from the reality and put a diferential robot in there with the aim to use an path planning algorithm through reinfor…
  ☆47Updated 5 years ago
• d1024choi / traj-pred-irl
  Official implementation of "Regularizing neural networks for future trajectory prediction via IRL framework" published in IET CV
  ☆36Updated 3 years ago
• ChanganVR / CADRL
  Implementation of paper "Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning". NO LONGER MAIN…
  ☆95Updated 7 years ago
• chengxuxin / DIRL_costmap
  Code to implement Maximum Entropy Deep Inverse Reinforcement Learning.
  ☆14Updated 5 years ago
• svip-lab / CIDNN
  CIDNN: Encoding Crowd Interaction with Deep Neural Network
  ☆74Updated 7 years ago
• ZijianHan / torcs-reinforcement-learning
  RL for path planning
  ☆13Updated 7 years ago
• cjy1992 / detect-loc-map
  End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
  ☆40Updated 3 years ago
• sujiongming / starcraftAI
  多智能体即时策略对抗方法与实践 苏炯铭 刘鸿福 陈少飞 项凤涛 编著 科学出版社 2019.11 随书代码
  ☆31Updated 5 years ago
• kanakkabara / Collision-Avoidance
  Collision Avoidance with Reinforcement Learning
  ☆22Updated 2 years ago
• svenkreiss / socialforce
  Differentiable Social Force simulation with universal interaction potentials.
  ☆138Updated last year
• jerrywiston / RL-Mapless-Navigation
  ☆20Updated 5 years ago
• miaoruonan / MACA_test
  ☆11Updated 4 years ago
• mit-acl / rl_collision_avoidance
  Training code for GA3C-CADRL algorithm (collision avoidance with deep RL)
  ☆130Updated 3 years ago
• nrhinehart / deep_imitative_models
  Reimplementation (currently partial) of Deep Imitative Models paper, ICLR '20
  ☆74Updated 3 years ago
• KarlXing / RLCodebase
  RLCodebase: PyTorch Codebase For Deep Reinforcement Learning Algorithms
  ☆28Updated 2 years ago
• NathanGavenski / IUPE
  Pytorch official implementation for Imitating Unknown Policies via Exploration.
  ☆14Updated 2 years ago
• MajidMoghadam2006 / gym-deepcars
  A simple highway traffic simulation for self-driving car agents in occupancy grid world
  ☆15Updated 6 years ago
• StanfordASL / Trajectron
  Code accompanying "The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs" by Boris Ivanovic an…
  ☆140Updated 5 years ago
• jastfkjg / DDPG_Torcs_PyTorch
  Using PyTorch and DDPG to play Torcs
  ☆54Updated 7 years ago
• sherrychen1120 / MIDAS
  Code for the paper: "MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation"
  ☆17Updated 4 years ago