AlphaStar - Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning
Explore DeepMind's AI agent achieving Grandmaster-level skill in StarCraft II through innovative multi-agent reinforcement learning and League Training techniques.
IMPALA - Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
Explore IMPALA, a distributed reinforcement learning architecture for efficient multi-task learning. Learn about V-trace, off-policy correction, and scalable performance across thousands of machines.
Reinforcement Learning, Fast and Slow
Explores recent advancements in deep reinforcement learning, discussing techniques for faster learning and their potential implications for cognitive science and neuroscience.
Imagination-Augmented Agents for Deep Reinforcement Learning
Explore innovative deep reinforcement learning architecture combining model-free and model-based approaches for improved performance and efficiency in complex environments.
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.
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.
Actor Critic
Learn about actor-critic methods in reinforcement learning, covering REINFORCE, temporal difference updates, and deterministic policy gradients for continuous action spaces.
Conversational Agents
Explore intent classification, named entity recognition, and neural text generation in dialogue systems, focusing on challenges and advancements in conversational AI.
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.
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.
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.
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.
Introduction to Reinforcement Learning
Explore reinforcement learning's evolution, breakthroughs, and applications in AI, from board games to robotics. Gain insights into key concepts, formulations, and future directions in this rapidly advancing field.
Task-Driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces
Explore task-driven network discovery using deep reinforcement learning on embedded spaces. Learn about improving network observability and optimizing data collection for accurate analysis.
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.
Fast Reinforcement Learning With Generalized Policy Updates
Explore advanced techniques in deep reinforcement learning, focusing on generalized policy updates for rapid learning and improved performance in complex environments.
Attacking the Off-Policy Problem With Duality
Explore deep reinforcement learning techniques to address off-policy challenges using duality principles, enhancing AI decision-making in complex environments.
Productionizing Deep Reinforcement Learning with Spark and MLflow
Aprenda a aplicar Deep Reinforcement Learning em escala usando Spark e MLflow para personaliza ção de jogos e aumento de engajamento, com dicas práticas para superar desafios de produção.
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Explores SINDy-RL, a framework combining sparse identification of nonlinear dynamics with reinforcement learning for efficient, interpretable control policies in complex environments.