Massively Parallel Deep Reinforcement Learning. 🔥

深度强化学习路径规划, SAC-Auto路径规划, Soft Actor-Critic算法, SAC-pytorch，激光雷达Lidar避障，激光雷达仿真模拟，Adaptive-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.

Robot navigation using deep reinforcement learning

SUMO Pytorch Deep Reinforcement Learning Traffic Signal Control

This repo implements Deep Q-Network (DQN) for solving the Frozenlake-v1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 in both 4x4 and 8x8 map sizes.

记录一些DRL算法实现

This repo implements Deep Q-Network (DQN) for solving the Mountain Car v0 environment (discrete version) of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 with a custom reward function for faster convergence.

This repo contains the Deep Reinforcement Learning algorithm Soft Actor Critic (SAC) implementation in PyTorch

🚦 Traffic Management System 🚏 With Deep Reinforcement Learning 🚗

Hosts my major and mini Deep Reinforcement learning 👨‍💻and Deep Learning projects 🔝

This repo implements Deep Q-Network (DQN) for solving the Cliff Walking v0 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 with the finest tuning.

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

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.

Multi-Agent DRL task analysed as a part of a course project for CS698R-21, IITK

经典强化学习、深度强化学习算法复现

This repo implements the REINFORCE algorithm for solving the Cart Pole V1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1.

Multi-agent reinforcement learning in Unity’s Soccer Twos environment using POCA. Features enhanced observation memory, custom reward shaping, and optimized training configurations. Analyzes ELO performance, computational efficiency, and training trade-offs. Based on Dennis Soemers’ ML-Agents fork.

Modular pytorch implementation of PPO including in depth commentary of implementation details!