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Popular-RL-Algorithms
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..Reinforcement_Learning_for_Traffic_Light_Control
Apply deep reinforcement learning methods including DQN, DDPG for traffic light control in simulation (discrete environment), to prove the 'Green Wave' phenomenon in intelligent traffic system.QT_Opt
Q-network with cross-entropy (CE) method for reinforcement learning.MARS
MARS is shortened for Multi-Agent Research Studio, a library for mulit-agent reinforcement learning research.Cascading-Decision-Tree
Open-source code for paper CDT: Cascading Decision Trees for Explainable Reinforcement LearningBenchmark-Efficient-Reinforcement-Learning-with-Demonstrations
Benchmark present methods for efficient reinforcement learning. Methods include Reptile, MAML, Residual Policy, etc. RL algorithms include DDPG, PPO.Robotic_Door_Opening_with_Tactile_Simulation
Official code (simulation part) for paper Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory Zihan Ding, Ya-Yen Tsai, Wang Wei Lee, Bidan Huang International Conference on Intelligent Robots and Systems (IROS) 2021nash-dqn
Official code of Nash-DQN for paper: Nash-DQN algorithm for two-player zero-sum Markov games, details see our paper: A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games. Zihan Ding, Dijia Su, Qinghua Liu, Chi JinRL_RLBench
Reinforcement Learning for RLBenchRL-with-AutoEncoder-for-Learning-from-Image-Pixels
RoboTinder
PointNet_Landmarks_from_Image
Meta-Learning-for-Reinforcement-Learning
UPESI
Code for paper Not Only Domain Randomization: Universal Policy with Embedding System Identification.Store
marl_torch
Robot_Learning
ion-trap-tomography-experiment
Glints-detection
Robot_Learning2
Robosuite-Panda-IK
Consistency_Model_For_Reinforcement_Learning
Official implementation for: Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning ICLR'24Love Open Source and this site? Check out how you can help us