Implemenation of DDPG with numpy only (without Tensorflow)
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.
Designing and Training DQN for Cart Pole problem
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.
π¦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.
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