Reinforcement Learning
Explore deep reinforcement learning concepts, from Q-functions to policy gradients, with applications in gaming, robotics, and AI. Gain insights into cutting-edge techniques and real-world implementations.
Gaming for Military Precision Decisions - Simon Lucas
Explore how Game AI and statistical forward planning algorithms can enhance military decision-making and operational planning, with insights from Simon Lucas.
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.
Unsupervised Doodling and Painting with Generative Agents
Explore unsupervised learning in generative art, reinforcement learning agents as image creators, and the potential of AI in artistic tools and creative processes.
Group Sparsity, World Model, and Generative Adversarial Networks
Explore advanced deep learning concepts: sparse auto-encoders, world models for autonomous control, and generative adversarial networks (GANs). Gain insights into cutting-edge AI techniques and their applications.
CS885 - Semi-Markov Decision Processes
Explore semi-Markov decision processes, reinforcement learning, and queueing theory. Learn about Bellman's equation and the option framework for advanced decision-making strategies.
Trust Region Policy Optimization
Explore trust region policy optimization in reinforcement learning, covering key concepts like policy gradient, KL-penalized problems, and the TRPO algorithm.
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.
Partially Observable RL
Explore partially observable reinforcement learning, covering Markov decision processes, hidden Markov models, and belief monitoring in this comprehensive lecture.
Bayesian RL
Explore Bayesian reinforcement learning techniques, including planning, sampling, and value iteration, to optimize decision-making in uncertain environments.
Model-Based RL
Explore model-based reinforcement learning, including complex models, Dyna algorithm, search trees, and Monte Carlo methods for effective planning and decision-making.
Bayesian and Contextual Bandits
Explore Bayesian and contextual bandits, covering multi-armed bandits, Bayesian learning, Thompson sampling, and contextual applications in this comprehensive lecture.
CS885: Multi-Armed Bandits
Explore multi-armed bandits, stochastic bandits, and online optimization strategies. Learn about exploration-exploitation trade-offs, regret, and epsilon-greedy approaches in practical applications.
Actor Critic
Learn about actor-critic methods in reinforcement learning, covering REINFORCE, temporal difference updates, and deterministic policy gradients for continuous action spaces.
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.
Deep Q-Networks
Learn advanced reinforcement learning techniques, focusing on Deep Q-Networks. Explore approximation, optimization, and practical implementations to enhance AI decision-making capabilities.
OpenAI Environments - Mike Rudd
Explore OpenAI environments, including robotics and retro games. Learn to build custom environments and understand reinforcement learning concepts like reward functions.
Conversational Agents
Explore intent classification, named entity recognition, and neural text generation in dialogue systems, focusing on challenges and advancements in conversational AI.
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.
Distributional RL
Explore distributional reinforcement learning, covering return distribution, policy evaluation, and the C51 algorithm, with insights into its advantages and applications in Atari games.