Reinforcement Learning
Explore Q-learning, deep Q networks, and reinforcement learning environments through hands-on tutorials and practical implementations.
TF-Agents - A Flexible Reinforcement Learning Library for TensorFlow
Explore TF-Agents, a flexible Deep Reinforcement Learning library for TensorFlow. Learn about recent advancements, implementation techniques, and how to jumpstart your RL projects using this modular open-source tool.
Population-Based Methods for Single- and Multi-Agent Reinforcement Learning - Lecture
Explore population-based methods for reinforcement learning, including MeanQ for single-agent scenarios and XDO for multi-agent adversarial environments. Gain insights into ensemble techniques and game theory applications.
Overview of Deep Reinforcement Learning Methods
Comprehensive exploration of deep reinforcement learning techniques, including Q-learning, actor-critic methods, policy networks, and gradient optimization for advanced AI decision-making and control systems.
Q-Learning: From Basics to Deep Q-Networks
Explore Q-Learning fundamentals, from Markov Decision Processes to Deep Q-Networks, in this comprehensive overview of reinforcement learning concepts and applications.
AI-Aided Design of Novel Inhibitors Against SARS-CoV-2
Explore AI-driven drug design for SARS-CoV-2, focusing on 3CLpro inhibitors. Learn about deep Q-learning networks, fragment-based design, and structure-based optimization for developing potential lead compounds.
Reinforcement Learning - Pac-Man
Explore reinforcement learning through building a Pac-Man-beating agent. Gain insights into Q-learning, deep Q-learning with neural networks, and practical implementation using libraries like Keras.