Topic: rllib Goto Github
Some thing interesting about rllib
Some thing interesting about rllib
rllib,Reinforcement learning algorithms in RLlib
User: 0xangelo
rllib,RL based simulation
User: 3neutronstar
rllib,Super Mario Bros training with Ray RLlib DQN algorithm
User: ahmetfurkandemir
rllib,A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.
User: chuacheowhuan
Home Page: https://chuacheowhuan.github.io
rllib,My attempt to reproduce a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning) using DDPPO (Decentralized & distributed proximal policy optimization) from ray[rllib].
User: chuacheowhuan
rllib,Sample setup for custom reinforcement learning environment in Sagemaker. This example uses Proximal Policy Optimization with Ray (RLlib).
User: chuacheowhuan
rllib,Dynamic multi-cell selection for cooperative multipoint (CoMP) using (multi-agent) deep reinforcement learning
Organization: cn-upb
Home Page: https://cn-upb.github.io/DeepCoMP/
rllib,An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
Organization: derwenai
Home Page: https://anyscale.com/academy
rllib,An introductory tutorial about leveraging Ray core features for distributed patterns.
Organization: derwenai
rllib,RLlib tutorials
Organization: derwenai
Home Page: https://ray.io
rllib,π¦ Traffic Management System π With Deep Reinforcement Learning π
User: dev0guy
rllib,:zap: :zap: ππ¦π¦π± ππ πππ¨π°π΅π³π’π₯πͺπ―π¨ πΈπͺπ΅π© ππ’πΊ πππ
User: draichi
Home Page: https://ray.readthedocs.io/en/latest/index.html
rllib,Deep Reinforcement Learning For Trading
User: druce
Home Page: https://alphaarchitect.com/2020/02/26/reinforcement-learning-for-trading/
rllib,Emergent Communication in RLlib
User: dylancope
rllib,Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning), Mesa (Agent-based modeling) and OpenAI Gym.
User: eescriba
rllib,Interactive Multi-Agent Reinforcement Learning Environment for the board game Cathedral using PettingZoo
User: elliottower
rllib,Interactive Multi-Agent Reinforcement Learning Environment for the board game Gobblet using PettingZoo.
User: elliottower
Home Page: https://elliottower.github.io/gobblet-rl/
rllib,An interface for hierarchical environments.
Organization: globalapptesting
rllib,Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning
User: goshaq
rllib,Training in bursts for defending against adversarial policies
Organization: humancompatibleai
rllib,reinforcement learning alogrithm implement with Ray
User: hybug
rllib,Learning various robotic manipulations tasks of the UR3
User: isk03276
rllib,Walkthroughs for DSL, AirSim, the Vector Institute, and more
User: jacopopan
rllib,Multi-agent Self-Play Reinforcement Learning Library
User: kajune
rllib,Deep Learning and Computational Intelligence final project (5.0) - Application of reinforcement learning for optimization of a racing line of a F1 car
User: kgolemo
rllib,Comparison of different Deep Reinforcement Learning (DRL) Frameworks. This repository includes "tf-agents", "RLlib" and will soon support "acme" as well.
User: kochlisgit
rllib,Construction of controllers for Shadow-Hand in Mujoco environment, using Deep Learning. 2 Different methods were used to create the controllers: a) Behavioral Cloning b) Deep Reinforcement Learning
User: kochlisgit
rllib,A multi-agent reinforcement learning environment inspired by social deduction games
User: leonzamel
rllib,An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium
User: marcescandell
Home Page: https://alpype.readthedocs.io/en/latest/
rllib,A modular framework designed to simulate economies and markets using Reinforcement Learning.
Organization: marketsai
rllib,Multi-Agent Reinforcement Learning Environment for the card game SkyJo, compatible with PettingZoo and RLLIB
User: michaelfeil
Home Page: https://michaelfeil.eu/skyjo_rl/
rllib,An autonomous driving simulator for modelling Vehicle to Infrastructure (V2I) conditions.
User: mynkpl1998
rllib,RL environment replicating the werewolf game to study emergent communication
User: nicofirst1
rllib,RL environments and trained agents in CARLA using RLlib
User: nisheeth-golakiya
rllib,Rllib framework for using Unreal Engine 5 (UE5) as external environment for Reinforced Learning training process
Organization: nullspace-colombia
rllib,SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
User: openpsi-projects
rllib,Tutorial for Ray
Organization: openrl-lab
Home Page: https://www.zhihu.com/column/c_1658439083574591488
rllib,VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
Organization: proroklab
Home Page: https://vmas.readthedocs.io
rllib,Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Organization: ray-project
Home Page: https://ray.io
rllib,One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
Organization: replicable-marl
Home Page: https://marllib.readthedocs.io
rllib,Simple Training and Evaluation of Multi-Agent Environments with Deep Reinforcement Algorithms π¨
User: resuldagdanov
rllib,Used Flow, Ray/RLlib and OpenAI Gym to simulate and train autonomous vehicles/human drivers in SUMO (Simulation of Urban Mobility)
User: rlew631
rllib,ray project δΈζζζ‘£
User: senmumu
rllib,An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.
User: stefanbschneider
Home Page: https://mobile-env.readthedocs.io
rllib,NIPS challenge 2018 Prosthetics playground and testing ideas
User: toanngosy
rllib,RL robust to deadlocks for the Flatland Challenge 2020.
User: wullli
rllib,RL training for the 6DoF manipulator
User: xdralex
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