Publication

Expertness Based Cooperative Q-Learning

Related publications (38)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Model-based reinforcement learning and navigation in animals and machines

Dane Sterling Corneil

For decades, neuroscientists and psychologists have observed that animal performance on spatial navigation tasks suggests an internal learned map of the environment. More recently, map-based (or model-based) reinforcement learning has become a highly activ ...

EPFL2018

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Ali H. Sayed

This work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information fr ...

IEEE2018

Coordinated Optimization and Control for Smart Grids

Altug Bitlislioglu

In this thesis, we consider commercial buildings with available heating, ventilation and air conditioning (HVAC) systems, and develop methods to assess and exploit their energy storage and production potential to collectively offer ancillary services to th ...

EPFL2018

Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer, Hadrien Hendrikx

In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to \textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learni ...

EPFL2017

Evidence for eligibility traces in human learning

Michael Herzog, Wulfram Gerstner, Kerstin Preuschoff, Marco Philipp Lehmann, He Xu, Vasiliki Liakoni

Whether we prepare a coffee or navigate to a shop: in many tasks we make multiple decisions before reaching a goal. Learning such state-action sequences from sparse reward raises the problem of credit-assignment: which actions out of a long sequence should ...

arXiv2017

Diffusion adaptation over networks

Ali H. Sayed

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self-organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with proce ...

Elsevier2014

Decentralized Anti-coordination Through Multi-agent Learning

Boi Faltings, Ludek Cigler

To achieve an optimal outcome in many situations, agents need to choose distinct actions from one another. This is the case notably in many resource allocation problems, where a single resource can only be used by one agent at a time. How shall a designer ...

AI Access Foundation2013

On the Influence of Informed Agents on Learning and Adaptation Over Networks

Ali H. Sayed

Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of ...

Institute of Electrical and Electronics Engineers, Inc., 345 E. 47 th St. NY NY 10017-2394 United States2013

Sparse reward processes

Christos Dimitrakakis

We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is a relation among those tasks, then the information gained during execution of one task has value for the execution of another task. Cons ...

arxiv2012

Reaching Correlated Equilibria Through Multi-agent Learning

Boi Faltings, Ludek Cigler

Many games have undesirable Nash equilibria. For exam- ple consider a resource allocation game in which two players compete for an exclusive access to a single resource. It has three Nash equilibria. The two pure-strategy NE are effi- cient, but not fair. ...

2011