A pytorch tutorial for DRL(Deep Reinforcement Learning)

Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.

A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar.

A short and easy implementation of Quantile Regression DQN | Distributional Reinforcement Learning

The implement of all kinds of dqn reinforcement learning with Pytorch

🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.

Collection of reinforcement learning algorithms implementations with TensorFlow2

Reinforcement learning algorithm implementation

Yet another deep reinforcement learning

Quantile Regression DQN implementation for bridge fleet maintenance optimization using Markov Decision Process. Migrated from C51 distributional RL (v0.8) with 200 quantiles and Huber loss. Features: Dueling architecture, Noisy Networks, PER, N-step learning. All 6 maintenance actions show positive returns with 68-78% VaR improvement.

SDF maps and URDF models for QR-DQN mobile robot path planning

Use Baseline3 zoo Framework and PPO/QR-DQN algo to train games like super mario tetris etc....

Multi-Equipment CBM (Condition-Based Maintenance) optimization using Deep Q-Learning with cost leveling and scenario comparison. Advanced RL system with QR-DQN, N-step learning, and parallel environments for HVAC equipment predictive maintenance.

Multi-Equipment CBM system using QR-DQN with advanced probability distribution analysis. Coordinated maintenance decision-making for 4 industrial equipment units with realistic anomaly rates (1.9-2.2%), comprehensive risk analysis (VaR/CVaR), and 51-quantile distribution visualization.

A Reinforcement Learning MVP (Minimum Viable Product) for Condition-Based Maintenance (CBM) using industrial equipment temperature sensor data. This project implements a sophisticated QR-DQN (Quantile Regression Deep Q-Network) agent to learn optimal maintenance policies balancing risk mitigation and cost minimization.