target-network

Implemenation of DDPG with numpy only (without Tensorflow)

Deep Q-Network experience replay buffer and Polyak target network averaging engine

Deep Q-Network experience replay buffer and Polyak target network averaging engine

PGuNN - Playing Games using Neural Networks

Using reinforcement learning method, Deep Q-Learning ("DQN") to have an agent learn an algorithm for automated stock trading.

Policy-Based Methods. Learn the theory behind evolutionary algorithms and policy-gradient methods. Design your own algorithm to train a simulated robotic arm to reach target locations.

Projet RL GridWorld 5x5 comparant trois agents : immédiat, périodique (avec replay memory) et simple (sans replay). Visualisation des scores et trajectoires, analyse de stabilité et performance. Basé sur Python et PyTorch, avec modèles sauvegardés et scripts d'entraînement/test.

Designing and Training DQN for Cart Pole problem

🐦A DQN agent that learned to play Flappy Bird from scratch . Built with PyTorch, experience replay, and target networks. Trained over 100K+ episodes on GPU. Includes pre-trained model, training logs, and the full optimization story of going from zero convergence to 101.9 reward.

Research-grade Reinforcement Learning framework for single-agent and multi-agent warehouse navigation using Deep Q-Networks (DQN), PyTorch, replay buffer, target networks, logging, and full test suite. Built for PhD-level RL and autonomous systems research.