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a-introduction-to-reinforcement-learning

RL library based on algorithms from the book <A-introduction-to-reinforcement-learning>

aa-sipp-m

Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.

agvs-public

多AGV路径规划算法研究

data_structure

datasturcture

distributedrl_mapf

Distributed RL/IL code for Multi-Agent Path Finding (MAPF)

entry1

graph

gym-collision-avoidance

leetcode

LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解，记录自己的leetcode解题之路。)

libmultirobotplanning

Library with search algorithms for task and path planning for multi robot/agent systems

multi_agent_path_planning

multiagent-pathfinding

Conflict-based search for multi-agent path finding

negotiation-based-marl

Source code for journal paper "Multiagent Reinforcement Learning With Sparse Interactions by Negotiation and Knowledge Transfer"

newdatastructure

reinforce

Reinforcement Learning Algorithm Package & PuckWorld, GridWorld Gym environments

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials

rl-book

Source codes for the book "Reinforcement Learning: Theory and Python Implementation"

rl-collision-avoidance

Implementation of the paper "Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning"

rvo2-3d

Optimal Reciprocal Collision Avoidance in Three Dimensions (C++)

solutions-of-reinforcement-learning-an-introduction-sutton-2nd

Solutions for Reinforcement Learning, An Introduction

src

SemaFORR + Menge in the ROS framework

test

test_git