TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
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Updated
Aug 19, 2024 - Python
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
A toolkit for reproducible reinforcement learning research.
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.
This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
Implementing Reinforcement Learning, namely Q-learning and Sarsa algorithms, for global path planning of mobile robot in unknown environment with obstacles. Comparison analysis of Q-learning and Sarsa
Multi-Objective Reinforcement Learning algorithms implementations.
Pytorch Implementation of Reinforcement Learning Algorithms ( Soft Actor Critic(SAC)/ DDPG / TD3 /DQN / A2C/ PPO / TRPO)
EasyRL: An easy-to-use and comprehensive reinforcement learning package.
self-studying the Sutton & Barto the hard way
Our VMAgent is a platform for exploiting Reinforcement Learning (RL) on Virtual Machine (VM) scheduling tasks.
Code for "Constrained Variational Policy Optimization for Safe Reinforcement Learning" (ICML 2022)
Reinforcement learning algorithms
RL-Toolkit: A Research Framework for Robotics
Reinforcement Learning framework for learning IoT interactions.
Toy case for learning through Reinforcement Learning algorithms how to establish TCP connections.
Deep Reinforcement Learning - Implementations and Theory: A path to mastery
Custom Reinforcement Learning Agents
Safe Reinforce Learning -> Constraint algorithms to train agents in Safety Gym, paper notes on research papers regarding RL with constraints + optimizer + neural networks, PyTorch implementation on policy gradient algorithms
Optimized version of the MinAtar (testbed for AI agents) codebase along with benchmarks for standard Reinforcement Learning agents on various environments.
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