Decision Awareness in Reinforcement Learning - End-to-End Optimization Approaches
Explore decision awareness in reinforcement learning, covering control-oriented models, neural ODEs, and computational challenges. Gain insights into advanced techniques for optimal decision-making.
Recent Advances in Functional Optimization
Explore cutting-edge functional optimization techniques for reinforcement and supervised learning, with theoretical insights and practical algorithms.
Representation-driven Option Discovery in Reinforcement Learning
Explore representation-driven option discovery in reinforcement learning, enhancing temporal abstraction and continual learning for intelligent agents.
Hindsight Learning for MDPs with Exogenous Inputs
Explore data-efficient algorithms for resource management problems using Hindsight Learning in Exo-MDPs, outperforming traditional methods in various applications including cloud resource allocation.
Large Neural Nets for Amortized Probabilistic Inference in Highly Multimodal Distributions
Explore amortized probabilistic inference using large neural networks for complex distributions. Learn about GFlowNets and their potential to improve AI systems with Bayesian and causal understanding.
Outracing Champion Gran Turismo Drivers With Deep Reinforcement Learning
Explore deep reinforcement learning techniques used to train AI agents that outperform world-class e-sports drivers in Gran Turismo racing simulations.
Introduction to SciPhy Reinforcement Learning - Part 1
Explore SciPhy RL: a neural network-based method for solving stochastic optimal control problems in continuous time, with applications and high-dimensional examples.
Machine Learning for Chip Design and Architecture
Explore machine learning advancements with Azalia Mirhoseini's insights at TMLS2019, gaining valuable knowledge from a leading expert in the field.
AI-Aided Design of Novel Inhibitors Against SARS-CoV-2
Explore AI-driven drug design for SARS-CoV-2, focusing on 3CLpro inhibitors. Learn about deep Q-learning networks, fragment-based design, and structure-based optimization for developing potential lead compounds.
The AI Economist: Economic Policy Design Using Deep Reinforcement Learning and AI Simulations
Explore AI-driven economic policy design using deep reinforcement learning and simulations. Learn how AI can improve equality, productivity, and public health outcomes in complex socio-economic challenges.
Reinforcement Learning via Stochastic Control
Explore reinforcement learning in continuous time and space, examining stochastic control theory's role and recent developments in this emerging field.
Towards Fast Autonomous Learners: Advances in Reinforcement Learning - 2015
Explore reinforcement learning for AI agents interacting with humans, focusing on sample efficiency and transfer learning to improve educational tools and decision-making systems.
Foundation Models and the Transfer of Embodied Autonomy
Explore challenges in developing foundation models for sequential decision-making, including transfer learning, and discuss future research directions in embodied autonomy.
Aligning AI to Everyone via Reinforcement Learning
Explore reinforcement learning techniques for aligning AI with human values, fostering cooperation and trust in intelligent decision-making systems.
RLHF: How to Learn from Human Feedback with Reinforcement Learning
Explore reinforcement learning techniques for incorporating human feedback in AI systems, enhancing multi-agent and human-AI interactions in social contexts.
Concordia Contest 2024 - Basic Agent Development Tutorial
Develop cooperative AI agents for text-based environments using the Concordia framework. Advance language model intelligence in open-ended worlds similar to tabletop RPGs.
Self-Play in Artificial Intelligence - Lecture by Noam Brown
Explore advanced AI techniques in imperfect-information games, focusing on self-play algorithms and their applications in poker and beyond.
Unsupervised Environment Design
Explore automatic generation of environments for training RL agents, from GenIE to Unsupervised Environment Design, bridging classical decision theory and deep learning.
Global Guarantees for Policy Gradient Methods
Explore global guarantees for policy gradient methods in reinforcement learning, examining theoretical foundations and practical implications.
Customizing Generative AI Models with Vertex AI
Explore Vertex AI's capabilities for customizing foundation models, including supervised fine-tuning and distillation techniques, to enhance performance and create unique AI offerings.
Scalable Advanced ML Systems with Ray, Google Kubernetes Engine, and ML Accelerators
Explore scalable ML systems using Ray, GKE, and ML accelerators. Learn to tackle challenges in training LLMs and distributing RL systems for advanced machine learning at scale.