Reinforcement Learning
Explore Q-learning, deep Q networks, and reinforcement learning environments through hands-on tutorials and practical implementations.
Reinforcement Learning in Artificial Intelligence - Full Course
Keeping the importance of Reinforcement Learning in mind, we have come up with this full course by Great Learning Faculty Dr. Narayana Darapaneni. Dr. Narayana holds a PhD in Mathematics from Pierre and Marie Curie University, Paris, France.
Machine Learning with Python - Tutorial for Beginners
Comprehensive Python-based machine learning tutorial covering data manipulation, visualization, statistics, and reinforcement learning, with practical examples and a smart taxi case study.
Reinforcement Learning - Machine Learning Meets Control Theory
Explore reinforcement learning's intersection with control theory, covering key concepts, algorithms, and applications in this comprehensive overview of machine learning's powerful technique.
Reinforcement Learning
Explora el Aprendizaje por Refuerzo, desde métodos básicos hasta redes neuronales y aplicaciones en dinámica de fluidos y control, en esta serie de 3.5 horas por Steve Brunton.
MDPs - Markov Decision Processes - Decision Making Under Uncertainty Using POMDPs.jl
Explore Markov Decision Processes and decision-making under uncertainty using POMDPs.jl. Learn about MDPs, Grid World, solvers, reinforcement learning, and visualization techniques in Julia.
Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin
Explore efficient reinforcement learning with linear function approximation, covering key concepts like sample efficiency, value-based algorithms, and exploration in multi-armed bandits and linear MDPs.
Reinforcement Learning
Explore reinforcement learning's foundations, applications, and advanced concepts, including Q-learning, exploration-exploitation trade-offs, and instance-optimality in policy evaluation.
Training Neural Networks for Temporal Difference Model Predictive Control - Part 2
Dive into the technical aspects of training neural networks in TD-MPC, covering TD learning, Q network optimization, and data collection strategies for robotic control systems.
Understanding Q* (Q-star) - From Q-Learning to Maximum Entropy Reinforcement Learning
Delve into the technical foundations of Q* (Q-star), exploring its roots in physics entropy, reinforcement learning principles, and practical applications in machine learning systems.
Challenges in Reward Design for Reinforcement Learning-based Traffic Signal Control - An Investigation Using CO2 Emission Objective
Explore the complexities of reward design in reinforcement learning for traffic signal control, focusing on CO2 emission reduction through Deep Q-Network implementation and reward optimization strategies.
Q Learning Tutorial - Training Loop
Explore the DQN training loop, covering policy implementation, reward calculation, and key steps in reinforcement learning algorithm implementation.
Q Learning Tutorial - Experience Replay
Explore Experience Replay in Q-Learning, focusing on data formatting and implementation. Learn key techniques for improving reinforcement learning algorithms in this hands-on tutorial.
Fast Reinforcement Learning With Generalized Policy Updates - Paper Explained
Explanation of a novel reinforcement learning framework that enables agents to transfer knowledge between tasks, potentially solving new problems with zero-shot learning through generalized policy updates.
Q-Learning - Model Free Reinforcement Learning and Temporal Difference Learning
Explore Q-learning and temporal difference algorithms in reinforcement learning, including SARSA and connections to biological learning through dopamine. Covers key concepts for deep reinforcement learning.
Model Based Reinforcement Learning - Policy Iteration, Value Iteration, and Dynamic Programming
Explore dynamic programming in model-based reinforcement learning, covering policy iteration, value iteration, and Q-learning for effective decision-making strategies.
Reinforcement Learning Series - Overview of Methods
Comprehensive overview of reinforcement learning methods, covering model-based and model-free approaches, from dynamic programming to deep RL and policy gradient optimization.
Introduction to Reinforcement Learning
Learn reinforcement learning fundamentals and apply them to solve control problems using Python libraries. Hands-on training with cartpole example and challenge to tackle inverted pendulum problem.
Playing Atari with Deep Reinforcement Learning - Paper Explained
Explore deep reinforcement learning's breakthrough in Atari game mastery using convolutional neural networks, revolutionizing AI with pixel-based control policies and outperforming human experts.
Thinking While Moving - Deep Reinforcement Learning with Concurrent Control
Explore concurrent control in reinforcement learning, where agents make decisions while executing actions, revolutionizing robotic movement and decision-making processes.
Deep Recurrent Q-Networks
Explore deep reinforcement learning with recurrent neural networks, focusing on LSTM units, Q-learning, and their applications in sequential decision-making problems.