Autonomous and Adaptive Systems 2020-21


The goal of this module is to provide a solid introduction to the design of autonomous and adaptive computing systems from a theoretical and practical point of view. Topics will include principles of autonomous system design, reinforcement learning, game-theoretic approaches to cooperation and coordination, bio-inspired systems, complex adaptive systems, and computational social systems. The module will also cover several practical applications from a variety of fields including but not limited to distributed and networked systems, mobile and ubiquitous systems, robotic systems, and vehicular and transportation systems.

Link to official course page containing syllabus and textbooks



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Introduction to the Course

Introduction to Intelligent and Autonomous Agents

Introduction to Reinforcement Learning

Introduction to Multi-Armed Bandits

Monte Carlo Methods

Temporal Difference Methods

Introduction to Deep Learning - First Part

Introduction to Deep Learning - Second Part

Introduction to Deep Learning - Third Part

Value Approximation Methods

Policy Gradient Methods

Introduction to TensorFlow and Keras

Introduction to Gym

Introduction to RL in TensorFlow - Advanced Topics

Multiagent Learning

Generative Machine Learning

Autonomous Robots and Self-driving Cars

Jupyter Notebooks

Notebook Keras MNIST

Notebook DQN Cartpole

Notebook Atari Game with Policy Network

Last updated: 8 May 2021.