rllib
There are 79 repositories under rllib topic.
ray-project/ray
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Replicable-MARL/MARLlib
One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
proroklab/VectorizedMultiAgentSimulator
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.
Draichi/T-1000
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ChuaCheowHuan/gym-continuousDoubleAuction
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.
stefanbschneider/mobile-env
An open, minimalist Gymnasium environment for autonomous coordination in wireless mobile networks.
druce/rl
Deep Reinforcement Learning For Trading
DerwenAI/ray_tutorial
An introductory tutorial about leveraging Ray core features for distributed patterns.
JacopoPan/a-minimalist-guide
Walkthroughs for DSL, AirSim, the Vector Institute, and more
DerwenAI/rllib_tutorials
RLlib tutorials
goshaQ/adaptive-tls
Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning
CN-UPB/DeepCoMP
Dynamic multi-cell selection for cooperative multipoint (CoMP) using (multi-agent) deep reinforcement learning
0xangelo/raylab
Reinforcement learning algorithms in RLlib
DerwenAI/gym_example
An example implementation of an OpenAI Gym environment used for a Ray RLlib tutorial
MarcEscandell/ALPypeRL
An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium
AhmetFurkanDEMIR/SuperMarioBrosRL
Super Mario Bros training with Ray RLlib DQN algorithm
rlew631/AutonomousVehicleSimulation
Used Flow, Ray/RLlib and OpenAI Gym to simulate and train autonomous vehicles/human drivers in SUMO (Simulation of Urban Mobility)
nicofirst1/rl_werewolf
RL environment replicating the werewolf game to study emergent communication
openpsi-projects/srl
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
OpenRL-Lab/Ray_Tutorial
Tutorial for Ray
akirasosa/aie-train
AI Economist Training Examples
dcos-labs/dcos-jupyterlab-service
JupyterLab Notebook for Mesosphere DC/OS
HumanCompatibleAI/better-adversarial-defenses
Training in bursts for defending against adversarial policies
elliottower/gobblet-rl
Interactive Multi-Agent Reinforcement Learning Environment for the board game Gobblet using PettingZoo.
kochlisGit/Deep-RL-Frameworks
Comparison of different Deep Reinforcement Learning (DRL) Frameworks. This repository includes "tf-agents", "RLlib" and will soon support "acme" as well.
eescriba/smart-cities-drl
Urban mobility simulations with Python3, RLlib (Deep Reinforcement Learning), Mesa (Agent-based modeling) and OpenAI Gym.
Nullspace-Colombia/unray-bridge
Rllib framework for using Unreal Engine 5 (UE5) as external environment for Reinforced Learning training process
ChuaCheowHuan/PBT_MARL_watered_down
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].
isk03276/LearnToMoveUR3
Learning various robotic manipulations tasks of the UR3
michaelfeil/skyjo_rl
Multi-Agent Reinforcement Learning Environment for the card game SkyJo, compatible with PettingZoo and RLLIB
Wadaboa/cpr-appropriation
Solutions to the Harvest CPR appropriation problem with policy gradient methods and social learning, for Autonomous and Adaptive Systems class at UNIBO
marketsAI/marketsAI
A modular framework designed to simulate economies and markets using Reinforcement Learning.
kochlisGit/Shadow-Hand-Controller
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
xdralex/pioneer
RL training for the 6DoF manipulator
elliottower/cathedral-rl
Interactive Multi-Agent Reinforcement Learning Environment for the board game Cathedral using PettingZoo
KGolemo/f1-racing-line-optimization
Deep Learning and Computational Intelligence final project (5.0) - Application of reinforcement learning for optimization of a racing line of a F1 car