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.

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

A Torch Based RL Framework for Rapid Prototyping of Research Papers

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

Implementation of Reinforcement Learning Based Autonmous Control of a TurtleBot2 as a substitute for a Formula SAE Car

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

Collection of Reinforcement Learning Algorithm implementations.

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

Implementation of TD3 agent in PyTorch.

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

Tests SOTA algorithms using pendulum as baseline environment

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