zachkeer

Deep-Reinforcement-Learning-Algorithms-with-PyTorch

PyTorch implementations of deep reinforcement learning algorithms and environments

ReAgent

A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)

tensorflow_practice

tensorflow实战练习，包括强化学习、推荐系统、nlp等

DeepRL-Agents

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

Distributed-MADDPG

Distributed Multi-Agent Cooperation Algorithm based on MADDPG with prioritized batch data.

gitbook2pdf

Grab the contents of the gitbook document and convert it to pdf

jebja

a small 2d sailing simulator in haxe/heaps

maddpg

Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

maddpg-replication

PARL

A high-performance distributed training framework for Reinforcement Learning

pytorch-doc-zh

Pytorch 中文文档

pytorch-handbook

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

pytorch-maddpg

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

pytorch-MADRL

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

ray

A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Recurrent-Multiagent-Deep-Deterministic-Policy-Gradient-with-Difference-Rewards

Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging simulated continuous control single agent tasks. These methods have further been extended to multiagent domains in cooperative, competitive or mixed environments. This paper primarily focuses on multiagent cooperative settings which can be modeled for several real world problems such as coordination of autonomous vehicles and warehouse robots. However, these systems suffer from several challenges such as, structural credit assignment and partial observability. In this paper, we propose Recurrent Multiagent Deep Deterministic Policy Gradient (RMADDPG) algorithm which extends Multiagent Deep Determinisitic Policy Gradient algorithm - MADDPG \cite{lowe2017multi} by using a recurrent neural network for the actor policy. This helps to address partial observability by maintaining a sequence of past observations which networks learn to preserve in order to solve the POMDP. In addition, we use reward shaping through difference rewards to address structural credit assignment in a partially observed environment. We evaluate the performance of MADDPG and R-MADDPG with and without reward shaping in a Multiagent Particle Environment. We further show that reward shaped RMADDPG outperforms the baseline algorithm MADDPG in a partially observable environmental setting.

rmaddpg

Recurrent MADDPG for Partially Observable and Limited Communication Settings

scalable_maddpg

scalable multi agents reinforcement learning

Sea-Patrol

A simple implementation of the graph ADT in java to solve a simple path finding problem in the North Sea

ShipAI

Ship simulafor in openai gym style

tianshou

An elegant, flexible, and superfast PyTorch deep reinforcement learning platform.

Torch-rl