🤖 Explore reinforcement learning techniques with projects including a taxi agent using Q-Learning and a DQN-based Space Invaders agent.

🤖 Implement classic and state-of-the-art deep reinforcement learning algorithms using clear PyTorch code for easy understanding and application.

🤖 Explore deep learning architectures like ANN, CNN, RNN, and LSTM to enhance your understanding of machine learning and neural networks.

🌊 Implement advanced algorithms for USV path planning using reinforcement and imitation learning, ensuring efficient and safe navigation in complex environments.

🚀 Explore algorithms interactively with our learning platform, designed for ease and engagement. Join us as we open source code and tutorials soon!

This project provides comprehensive solutions for vehicle routing, delivery scheduling, and optimization in logistics operations.

🎮 Train a Deep Q-Learning agent using TensorFlow to master Atari Breakout with efficient experience replay and modular architecture for easy customization.

🤖 Analyze financial data with Dexter, an autonomous research agent that plans and learns to deliver accurate, data-driven insights.

🐍 Implement deep Q-learning to enhance the Snake game experience, enabling smarter gameplay strategies and improved learning performance.

🤖 Leverage continuous-time reinforcement learning to optimize asset-liability management and enhance financial decision-making.

🚀 Optimize your portfolio with deep reinforcement learning, achieving superior returns and risk management in dynamic asset allocation.

🔍 Explore machine learning by building algorithms from scratch in Python, comparing results with existing libraries, and enhancing your understanding.

🔋 Optimize battery dispatch using a rainflow-aware DQN that tracks degradation and enhances performance with advanced features for reliable results.

This project explores the application of reinforcement learning to Ludo, a stochastic multi-agent board game. I implement and compare several RL algorithms including tabular Q-learning, deep Q-networks (DQN), Dueling DQN, and rule-based heuristics.

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

Machine learning library, Distributed training, Deep learning, Reinforcement learning, Models, TensorFlow, PyTorch

This project is focussed on how coevolution and cooperation can emerge through multi-agent deep reinforcement-learning.

Lua-Based Machine Learning, Deep Learning And Reinforcement Learning Library (For Roblox And Pure Lua). Contains Over 95 Models!

Open-source simulator for autonomous driving research.

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.