Model-Based Reinforcement Learning with Value-Targeted Regression
Explore model-based reinforcement learning with value-targeted regression, covering episodic RL, UCRL, deterministic systems, and the MatrixRL algorithm.
Deep Robust Reinforcement Learning and Regularization
Explore robust reinforcement learning, addressing uncertainties in deep RL through regularization techniques. Gain insights into robust MDPs, policy evaluation, and uncertainty-aware Bellman equations.
Language as a Scaffold for Reinforcement Learning
Explore how natural language can enhance reinforcement learning, covering topics like instructions as observations, task decomposition, and learning from demonstrations.
Learning Exploration Strategies with Meta-Reinforcement Learning
Discover meta-reinforcement learning strategies for efficient exploration in complex environments, focusing on posterior sampling and task-relevant information.
Discussion - Offline Reinforcement Learning
Explore offline reinforcement learning, covering data sets, optimism under uncertainty, state coverage, and policy evaluation. Gain insights into pragmatic and conceptual approaches.
Offline Deep Reinforcement Learning Algorithms
Explore offline deep reinforcement learning algorithms, their challenges, and solutions like conservative Q-learning, with insights on modern machine learning and overcoming distributional shift.
The AI Economist- Improving Equality and Productivity with AI-Driven Tax Policies
Explore AI-driven tax policies for improved economic equality and productivity, featuring deep learning, reinforcement learning, and multi-agent simulations in a simplified economic model.
Batch Offline Reinforcement Learning - Part 1
Explore offline reinforcement learning theory, covering key concepts, challenges, and evaluation methods for developing AI systems using pre-collected data.
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.
Online Learning and Bandits - Part 2
Explore advanced bandit algorithms, including Exponential Weights, UCB, and Thompson Sampling. Learn design principles and analysis techniques for optimal decision-making in uncertain environments.
Planning and Markov Decision Processes - Part 2
Explore advanced concepts in reinforcement learning, including generative models, sample complexity, value equations, and linear programming, with experts from leading institutions.
Multiagent Reinforcement Learning: Rollout and Policy Iteration
Explore finite-state multiagent reinforcement learning through policy iteration and rollout, focusing on trading off control and state complexity in infinite horizon problems.
Mixed Autonomy Traffic: A Reinforcement Learning Perspective
Explore reinforcement learning for mixed autonomy traffic, focusing on counterfactual reasoning, urban simulation, and transfer learning to address societal challenges in transportation.
MOPO - Model-Based Offline Policy Optimization
Learn about MOPO, a model-based approach for offline reinforcement learning, addressing challenges in distributional domain shift and improving policy optimization.
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.
Statistical Considerations in Reinforcement Learning - Part 1a
Explore statistical considerations in reinforcement learning, covering inverse RL, high-stakes problems, and asymptotic frameworks for parameter estimation and uncertainty quantification.
Multi-Agent Reinforcement Learning - Part I
Explore multi-agent reinforcement learning, covering game theory, interaction protocols, and Nash equilibrium. Gain insights into challenges and formulations in this emerging field.
Formal Languages and Automata for Reward Function Specification and Efficient Reinforcement Learning
Explore formal languages and automata for specifying reward functions in reinforcement learning, focusing on Linear Temporal Logic and Reward Machines to address real-world RL challenges.
Backpropagation and Deep Learning in the Brain
Explore deep learning in the brain, backpropagation, and credit assignment algorithms. Discover neuroscience evidence and new models of neurons in this computational brain theory talk.
Practical Model-Based Algorithms for Reinforcement Learning and Imitation Learning
Explore efficient model-based algorithms for reinforcement and imitation learning, addressing challenges in learning dynamics and uncertainty, with practical examples and evaluations.