Explores curriculum learning strategies for training reinforcement learning models more efficiently, from simple to complex tasks.
Explores meta reinforcement learning, where agents learn to adapt quickly to new, unseen RL tasks, aiming for general-purpose problem-solving algorithms.
Explores efficient state representations for robots to accelerate Reinforcement Learning training, comparing pixel-based and model-based approaches.
Explores domain randomization as a technique to bridge the simulation-to-reality gap in robotics and deep reinforcement learning.
Introduces PlaNet, a model-based AI agent that learns environment dynamics from pixels and plans actions in latent space for efficient control tasks.
Explores how uncertainty modeling in recommender systems helps balance exploring new items versus exploiting known high-performing ones.
A review and tips for Georgia Tech's OMSCS CS7642 Reinforcement Learning course, covering workload, projects, and key learnings.
Step-by-step guide to reproducing the 'World Models' AI experiments, including prerequisites, software setup, and instructions for running pre-trained models.
A comprehensive overview of policy gradient algorithms in reinforcement learning, covering key concepts, notations, and various methods.
An introductory guide to Reinforcement Learning (RL), covering key concepts, algorithms like SARSA and Q-learning, and its role in AI breakthroughs.
Explores the Multi-Armed Bandit problem, a classic dilemma balancing exploration and exploitation in decision-making algorithms.
Explores applying Evolution Strategies (ES) to reinforcement learning problems for finding stable and robust neural network policies.
A visual guide explaining Evolution Strategies (ES) as a gradient-free optimization alternative to reinforcement learning for training neural networks.
A detailed review and explanation of key research papers in the field of Reinforcement Learning, part of a deep learning series.
