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Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.
Deep Reinforcement Learning for mobile robot navigation in IR-SIM simulation. Using DRL (SAC, TD3, PPO, DDPG) 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 DRL (SAC, TD3) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.
IEEE WCNC 2023: Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surfaces
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 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
🤖 TD3 implementation for Pendulum-v1 using PyTorch. Features twin critics, delayed policy updates, target policy smoothing, and clipped double Q-learning to address function approximation errors.
Performance evaluation of several DRL algorithms in a discrete action-space for resource allocation in Open RAN
A codebase for continuous action spaces Reinforcement Learning algorithms
Aligning an optical interferometer with beam divergence control and continuous action space.
Implementation of Reinforcement Learning Based Autonmous Control of a TurtleBot2 as a substitute for a Formula SAE Car
tabular and deep rl algorithms
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.
Repository contains codes for the course CS780: Deep Reinforcement Learning
TD3 Reinforcement Learning for Obstacle Avoidance in Ackermann-Steering Robots
Implementation of TD3 agent in PyTorch.
Teaching an bipedal bot how to walk using a TD3 algorithm (variant of Reinforcement Learning - Actor & Critic method)
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.
Tests SOTA algorithms using pendulum as baseline environment
Project for Artificial Intelligence course at University of Ljubljana, Faculty of Computer and Information science.
A novel and efficient methodology that enables the robot to maneuver safely through dense crowds in more ‘human-like’ patterns.
TD3 and PPO implementation -- Final project for the course ELEC-E8125 Reinforcement Learning at Aalto University
Twin Delayed Deep Deterministic Policy Gradients (TD3) implementation on Gymnasium robotics Fetch-Reach environment (Pytorch)
The pytorch implementation of td3
Project files of CS780: Deep Reinforcement Learning
Off-policy RL (DDPG, TD3, SAC) algorithms from scratch
A PyTorch implementation of the DRL algorithm TD3