Novelty of Behaviour as a Basis for the Neuro-evolution of Operant Reward Learning
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.
Learning how to act and adapting to unexpected changes are remarkable capabilities of humans and other animals. In the absence of a direct recipe to follow in life, behaviour is often guided by rewarding and by surprising events. A positive or a negative o ...
This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of strea ...
In reinforcement learning (RL), an agent makes sequential decisions to maximise the reward it can obtain from an environment. During learning, the actual and expected outcomes are compared to tell whether a decision was good or bad. The difference between ...
How do neurons orchestrate behaviour? Let us trace our footsteps back to one of the first hypotheses proposing an alliance between the brain and behaviour. This article trace Donald Hebb's life, and how, in the neurophysiology milieu of the 20th century, h ...
Multiple object tracking is a crucial Computer Vision Task. It aims at locating objects of interest in the image sequences, maintaining their identities, and identifying their trajectories over time. A large portion of current research focuses on tracking ...
Reducing the role of the car in daily mobility practices is still largely a struggle for Switzerland's public transport policies. Adapting transport supply to user demand is the major challenge for this modal shift, the response to which requires a detaile ...
We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable i ...
When humans or animals perform an action that led to a desired outcome, they show a tendency to repeat it. The mechanisms underlying learning from past experience and adapting future behavior are still not fully understood. In this thesis, I study how huma ...
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 ...
Learning to achieve one’s goal in a complex environment is a complicated task. In reinforcement learning (RL) tasks, an agent interacts with the environment to learn optimal actions. In humans, striatal areas are strongly involved in these tasks. During ag ...
