MIT: Deep Reinforcement Learning
Explore deep reinforcement learning concepts, algorithms, and applications, including Q-learning, policy gradients, and breakthroughs like AlphaGo and AlphaZero in game AI.
Reproducible, Reusable, and Robust Reinforcement Learning - Joelle Pineau
Explore reproducibility, reusability, and robustness in reinforcement learning. Delve into policy gradient methods, hyperparameter interplay, fair comparisons, and real-world applications of RL algorithms.
Policy Gradient
Learn about policy gradient methods in reinforcement learning, covering stochastic policies, supervised learning, and the stochastic gradient policy theorem with applications to AlphaGo.
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.
Deep Reinforcement Learning of Marked Temporal Point Processes
Explore deep reinforcement learning for marked temporal point processes, covering event representation, policy optimization, and applications in information propagation and knowledge creation.
Reinforcement Learning - MIT 6.S191 Lecture 5
Comprehensive exploration of deep reinforcement learning, covering Q-functions, policy gradients, and real-world applications like AlphaGo. Ideal for understanding RL fundamentals and advanced concepts.
Quantum Policy Gradient Algorithms for Reinforcement Learning
Delve into quantum policy gradient algorithms and their application in reinforcement learning, exploring quantum-classical speed-ups and parameterized quantum circuits for AI advancement.
Global Guarantees for Policy Gradient Methods
Explore global guarantees for policy gradient methods in reinforcement learning, examining theoretical foundations and practical implications.
Reinforcement Learning and Architectures of Intelligence - Part 1
Explore reinforcement learning and intelligence architectures with Microsoft Research's Ida Momennejad in this comprehensive tutorial from IPAM's Mathematics of Intelligences series.