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.
Formal Verification and Learning of Complex Systems - Professor Alessandro Abate
Exploring formal verification techniques for complex systems, combining model-based and data-driven methods to address scalability issues and provide system-level assertions in Cyber-Physical Systems.
Partially Observable RL
Explore partially observable reinforcement learning, covering Markov decision processes, hidden Markov models, and belief monitoring in this comprehensive lecture.
Markov Decision Processes
Explore Markov Decision Processes, their components, and applications in inventory management. Learn about rewards, discounting, and Bellman's equation for optimal decision-making.
Partially Observable Reinforcement Learning
Explore partially observable reinforcement learning, covering Markov processes, hidden models, and recurrent neural networks for decision-making in uncertain environments.
Introduction to RL
Learn key concepts of reinforcement learning including Markov decision processes, policy optimization, and model-free evaluation through lectures and examples like the inverted pendulum and toy maze.
Reinforcement Learning via an Optimization Lens
Explore reinforcement learning through an optimization lens, covering MDPs, Bellman equations, and novel approaches to solving RL problems using linear programming and primal-dual formulations.
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes
Explore policy gradient methods in Markov Decision Processes, covering optimality, approximation, and key concepts like entropy regularization and natural policy gradient.
On the Hardness of Reinforcement Learning With Value-Function Approximation
Explore challenges in reinforcement learning with value-function approximation, focusing on batch learning in large MDPs and the limitations of realizability assumptions.
Reinforcement Learning in Feature Space: Complexity and Regret
Explore reinforcement learning in feature space, focusing on complexity, regret analysis, and innovative approaches like MatrixRL. Learn about state feature mapping and its applications in real-world scenarios.
Online Learning in Markov Decision Processes - Part 2
Explore advanced concepts in Markov Decision Processes, including adversarial scenarios, regret analysis, and algorithms for online learning in dynamic environments.
Online Learning in Markov Decision Processes - Part 1
Explore online learning in Markov Decision Processes, covering theory, algorithms, and optimal strategies for reinforcement learning in dynamic environments.
Planning and Markov Decision Processes - Part 1
Explore fundamental concepts of planning and Markov Decision Processes, including control objectives, randomizing policies, and basic methods for reinforcement learning theory.
On the Statistical Complexity of Reinforcement Learning
Explores statistical complexities in reinforcement learning, covering sample complexity, regret analysis, and off-policy evaluation. Discusses minimax-optimal approaches and function approximation in MDPs.
Generalization and Robustness in Offline Reinforcement Learning
Explore offline reinforcement learning for safety-critical applications, covering generalization, robustness, and imitation learning in large-scale settings with rich function approximation.
2019 ADSI Summer Workshop- Algorithmic Foundations of Learning and Control
Explore uncertainty in perception for control systems, covering model-free algorithms, pixel-based control, and machine learning applications in robotics and autonomous systems.
Reinforcement Learning - Emma Brunskill - Stanford University
Explore reinforcement learning fundamentals, applications, and challenges with Stanford's Emma Brunskill, covering AI planning, machine learning, and decision-making processes.
Fast Learning of Small Strategies - Jan Křetínský, Technical University of Munich
Discover fast learning techniques for small strategies in Markov decision processes, combining verification precision with machine learning scalability for ε-optimal strategies and concise representation.
Theoretical Foundations of Reinforcement Learning
Explore key concepts and mathematical principles underlying reinforcement learning algorithms, including Markov decision processes, value functions, and policy optimization techniques.
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.
Reinforcement Learning IV - Advanced Concepts and Applications
Explore advanced reinforcement learning concepts with Stanford's Emma Brunskill, covering cutting-edge techniques and applications in AI decision-making.