Multi-agent reinforcement learning

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Related lectures (25)

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Reinforcement Learning: BackUp Diagrams

Introduces the BackUp diagram as a key graphic representation in reinforcement learning.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Model-Based Deep RL: Planning and VAST

Covers model-based reinforcement learning, planning, variational state tabulation, and efficient Q- and V-values updating.

Learning-aided Program Reasoning

Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.

Proximal Policy Optimization for Continuous Control

Explores Proximal Policy Optimization for enhancing stability and efficiency in continuous control with deep reinforcement learning.

Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

Covers policy gradient methods, focusing on direct action learning and optimizing rewards in reinforcement learning.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Deep Reinforcement Learning: Mini-Batches and Policy Methods

Discusses deep reinforcement learning methods, focusing on mini-batches and the implications of on-policy and off-policy training techniques.

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including Markov Decision Processes and policy gradient methods, and explores real-world applications and recent advances.

Mini-Batches in On- and Off-Policy Deep Reinforcement Learning

Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.