Pinned Repositories
baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Carla_Ray_Rlib
Carla-Simulator environment compatible with Ray/Rllib
CollaQ
A code implementation for our arXiv paper "Multi-agent Adhoc Team Play using Decompositional Q function"
CPU-Simulator
A simulator for instructions run in the cpu
Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
GamePlane
A small game for Android mobile phone
pgddpg
This article is the additional experimental details of paper 'Independent Deep Deterministic Policy Gradient Reinforcement Learning in Cooperative Multiagent Pursuit Games'. In this article, we mainly tests and compares the hyperparameter of the algorithm, and the performance of the algorithms in the case of different preys. The results show that PGDDPG, we proposed, is insensitive to hyperparameter and has stronger robustness which Has a good performance against different levels of prey.
PGddpg-conference
SmartCar
Historical data of 2014 and 2015 smart car competition
zhoushiyang12.github.io
Welcome to my blog~
zhoushiyang12's Repositories
zhoushiyang12/zhoushiyang12.github.io
Welcome to my blog~
zhoushiyang12/baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
zhoushiyang12/Carla_Ray_Rlib
Carla-Simulator environment compatible with Ray/Rllib
zhoushiyang12/CollaQ
A code implementation for our arXiv paper "Multi-agent Adhoc Team Play using Decompositional Q function"
zhoushiyang12/CPU-Simulator
A simulator for instructions run in the cpu
zhoushiyang12/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
PyTorch implementations of deep reinforcement learning algorithms and environments
zhoushiyang12/GamePlane
A small game for Android mobile phone
zhoushiyang12/pgddpg
This article is the additional experimental details of paper 'Independent Deep Deterministic Policy Gradient Reinforcement Learning in Cooperative Multiagent Pursuit Games'. In this article, we mainly tests and compares the hyperparameter of the algorithm, and the performance of the algorithms in the case of different preys. The results show that PGDDPG, we proposed, is insensitive to hyperparameter and has stronger robustness which Has a good performance against different levels of prey.
zhoushiyang12/PGddpg-conference
zhoushiyang12/SmartCar
Historical data of 2014 and 2015 smart car competition
zhoushiyang12/Deep-reinforcement-learning-with-pytorch
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....
zhoushiyang12/deeplearning-models
各种深度学习结构、模型和技巧的集合
zhoushiyang12/GitTest
Just have a try
zhoushiyang12/gym-carla
An OpenAI gym wrapper for CARLA simulator
zhoushiyang12/Hungry-goose-try
zhoushiyang12/MaCA
zhoushiyang12/MD-MADDPG
zhoushiyang12/multiagent-particle-envs
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
zhoushiyang12/PettingZoo
Gym for multi-agent reinforcement learning
zhoushiyang12/pytorch-maddpg
A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)
zhoushiyang12/RFQ-RFAC
Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning
zhoushiyang12/RLs
Reinforcement Learning Algorithms：SAC, TD3, TAC
zhoushiyang12/rosflight_ws
A full rosplane/rosflight workspace for class demos and getting started
zhoushiyang12/rosplane
A basic fixed-wing autopilot for ROS - based on the textbook "Small Unmanned Aircraft: Theory and Practice" by Randy Beard and Tim McLain
zhoushiyang12/SOTA-RL-Algorithms
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
zhoushiyang12/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
zhoushiyang12/tensorlayer
Deep Learning and Reinforcement Learning Library for Scientists 🔥