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Neural Correlates of Reinforcement Learning: eligibility trace, reward prediction error, novelty and surprise

Related publications (62)

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No-Regret Learning in Unknown Games with Correlated Payoffs

Maryam Kamgarpour, Andreas Krause, Ilija Bogunovic

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 ...

Curran Associates, Inc.2019

One-shot learning and eligibility traces in sequential decision making

Marco Philipp Lehmann

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 ...

EPFL2018

Time course of prediction errors in sequential decision making

Michael Herzog, He Xu

In reinforcement learning, an agent makes sequential decisions to maximize reward. During learning, the actual and expected outcome are compared to tell whether a decision was good or bad. The difference between the actual outcome and expected outcome is t ...

2018

Belief as a Wise Wager

Leyla Loued-Khenissi

From the moment we wake up in the morning to the day's ebb when we settle in to sleep, we are bound to the task of decision-making. Some of these decisions barely register in our consciousness, if at all, while others, less shy, take a more prominent place ...

EPFL2018

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

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

A two-stage model of decision making

Michael Herzog, Adrien Christophe Doerig, Leila Drissi Daoudi - Kleinbauer

The drift diffusion model (Ratcliff, 1978) is widely used to model binary decision-making. In this model, evidence for the two alternatives is integrated over time until it hits a decision boundary leading to a reaction. The drift diffusion model fits many ...

2017

What to Choose Next? A Paradigm for Testing Human Sequential Decision Making

Michael Herzog, Aaron Michael Clarke, Elisa Tartaglia

Many of the decisions we make in our everyday lives are sequential and entail sparse rewards. While sequential decision-making has been extensively investigated in theory (e.g., by reinforcement learning models) there is no systematic experimental paradigm ...

2017

Learning with Surprise

Mohammadjavad Faraji

Everybody knows what it feels to be surprised. Surprise raises our attention and is crucial for learning. It is a ubiquitous concept whose traces have been found in both neuroscience and machine learning. However, a comprehensive theory has not yet been de ...

EPFL2016

Reinforcement learning: the effect of environment

Michael Herzog, He Xu

Reinforcement learning is a type of supervised learning, where reward is sparse and delayed. For example in chess, a series of moves is made until a sparse reward (win, loss) is issued, which makes it impossible to evaluate the value of a single move. Stil ...

2016