With this function you can construct your weekly calendar of lessons, which is customized on the basis of the courses that you intend to follow. Warning: the personal schedule does not replace the presentation of the study plan! It's an informal tool that can help you better manage the organization of class attendance before the study plan presentation. After the study plan presentation we recommend you to use the Lecture timetable service in your Online Services.
To create your customized schedule follow these instructions:
Click on the "Enable" link to proceed. You will be asked your surname and first name in order to determine your alphabetic grouping.
To add or remove courses from your personal schedule, use the small icons which are found next to the courses:
addition of the course
removal of the course
selection of the section of the Laboratory of Architecture (Note: the effective area in which the teaching will be carried out will be determined after the presentation of the Study Plans)
The sidebar on the left displays the number of lessons included in schedule. There are also these commands:
View the schedule: allows the viewing of the weekly synoptic schedule
Delete the schedule: cancels the selections made
When you have finished the entry, you can print the calendar you have made.
Course completely offered in italian
Course completely offered in english
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching. These CFUs include:
Subject taught jointly with companies or organizations
Blended Learning & Flipped Classroom
Massive Open Online Courses (MOOC)
School of Industrial and Information Engineering
(Master of Science degree)(ord. 270) - MI (434) Engineering of Computing Systems
T2A - Sistemi e applicazioni dell'Informatica
Credits (CFU / ECTS)
Reinforcement learning deals with solving sequential decision making problems, when no (or minimal) prior information is available.
Solving sequential decision making problems means to find their optimal control policies.
Using reinforcement-learning algorithms, the optimal policy is learned through the direct interaction between the agent (or controller) and the system to be controlled.
The course will introduce the main modeling frameworks, will analyze the most relevant reinforcement-learning techniques, and, finally, some interesting applications of these techniques to real-world domains will be shown.
* Finite Markov Decision Processes
* Continuous Markov Decision Processes
* Partially Observable Markov Decision Processes
* Semi Markov Decision Processes
* Markov Games
* Value Iteration based algorithms (Q-learning, SARSA, TD(lambda))
* Policy Iteration based algorithms (actor-critic methods, LSPI)
* Policy Search algorithms (policy gradient methods and stochastic search techniques)
* Exploration techniques (R-MAX, model-based Interval Estimation)
* Model-free vs Model-based algorithms
* Batch algorithms (Fitted Q-iteration)
* Function approximation in Reinforcement Learning algorithms
* Hierarchical Learning (options, HAMs, MAX-Q)
* Multi-Agent Learning techniques (basic elements)
* Autonomic Computing
* Robot Control
* Water Resources Management