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XuYipeng's Projects

pymarl icon pymarl

Python Multi-Agent Reinforcement Learning framework

pytorch-a2c-ppo-acktr-gail icon pytorch-a2c-ppo-acktr-gail

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

pytorch-maddpg icon pytorch-maddpg

A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)

rl-adventure-2 icon rl-adventure-2

PyTorch0.4 implementation of: actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay

rl-baselines-zoo icon rl-baselines-zoo

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

rlkit icon rlkit

Collection of reinforcement learning algorithms

safety-starter-agents icon safety-starter-agents

Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.

spinningup icon spinningup

An educational resource to help anyone learn deep reinforcement learning.

stablerl_voltagectrl icon stablerl_voltagectrl

This repository contains source code necessary to reproduce the results presented in the following paper: Stability Constrained Reinforcement Learning for Real-Time Voltage Control (https://arxiv.org/pdf/2109.14854.pdf)

starcraft icon starcraft

Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II

supports_for_epc icon supports_for_epc

电力建设论文《基于价值认同的需求侧电能共享分布式交易策略》的支撑文件

supports_for_pst_paper icon supports_for_pst_paper

论文《考虑实时市场联动的电力零售商鲁棒定价策略》的支撑文件,拟发表在《电网技术》杂志。2021年10月23日。

t-maac icon t-maac

Transformer-based Multi-Agent Actor-Critic Framework

td3 icon td3

Author's PyTorch implementation of TD3 for OpenAI gym tasks

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