Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

IEEE WCNC 2023: Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces

A Torch Based RL Framework for Rapid Prototyping of Research Papers

强化学习算法库，包含了目前主流的强化学习算法(Value based and Policy based)的代码，代码都经过调试并可以运行

Deep Reinforcement Learning for mobile robot navigation in ROS2 Gazebo simulator. Using DRL (SAC, TD3) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Deep Reinforcement Learning for mobile robot navigation in ROS2 Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

A Decentralized, Fully Autonomous Drone Delivery System for Reliable, Efficient Transport of Goods

A codebase for continuous action spaces Reinforcement Learning algorithms

Performance evaluation of several DRL algorithms in a discrete action-space for resource allocation in Open RAN

Aligning an optical interferometer with beam divergence control and continuous action space.

Develop and implement reinforcement learning for real-world navigation in DuckieTown, optimizing performance and resilience for reliable autonomous movement, backed by interpretable decision-making tools.

tabular and deep rl algorithms

Repository contains codes for the course CS780: Deep Reinforcement Learning

A novel and efficient methodology that enables the robot to maneuver safely through dense crowds in more ‘human-like’ patterns.

Implementation of TD3 agent in PyTorch.

Tests SOTA algorithms using pendulum as baseline environment

Project for Artificial Intelligence course at University of Ljubljana, Faculty of Computer and Information science.

The pytorch implementation of td3

Twin Delayed DDPG

An adaptive Machine Reinforcement Learning (MRL) system is being developed to gather and analyze media data using web scraping, training models to predict outcomes in areas like stock market trends, sports events, and other performance domains. It continuously refines its strategies based on real-time data and evolving patterns.