Add to ORKGIn reinforcement learning (RL), a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experi-ence with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of RL in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in gre...
We argue that human inductive generalization is best explained in a Bayesian framework, rather than ...
In reinforcement learning (RL), an agent makes sequential decisions to maximise the reward it can ob...
Understanding the mechanisms behind learning and decision making under uncertainty remains an open c...
Reinforcement learning models of human and animal learning usually concentrate on how we learn the r...
In our everyday lives, we must learn and utilize context-specific information to inform our decision...
Computational models of learning have proved largely successful in characterizing potential mechanis...
How do people decide whether to try out novel options as opposed to tried-and-testedones? We argue t...
How do people decide whether to try out novel options as opposed to tried-and-tested ones? We argue ...
The field of Reinforcement Learning is concerned with teaching agents to take optimal decisions t...
Balancing exploration and exploitation is one of the central problems in reinforcement learning. We ...
Computational models of learning have proved largely successful in characterising potentialmechanism...
Computational models of learning have proved largely successful in characterizing potential mechanis...
Reinforcement learning algorithms have provided useful insights into human and an- imal learning and...
In this thesis we aim to improve generalisation in deep reinforcement learning. Generalisation is a ...
Abstract The complex behaviors we ultimately wish to understand are far from those currently used...
We argue that human inductive generalization is best explained in a Bayesian framework, rather than ...
In reinforcement learning (RL), an agent makes sequential decisions to maximise the reward it can ob...
Understanding the mechanisms behind learning and decision making under uncertainty remains an open c...
Reinforcement learning models of human and animal learning usually concentrate on how we learn the r...
In our everyday lives, we must learn and utilize context-specific information to inform our decision...
Computational models of learning have proved largely successful in characterizing potential mechanis...
How do people decide whether to try out novel options as opposed to tried-and-testedones? We argue t...
How do people decide whether to try out novel options as opposed to tried-and-tested ones? We argue ...
The field of Reinforcement Learning is concerned with teaching agents to take optimal decisions t...
Balancing exploration and exploitation is one of the central problems in reinforcement learning. We ...
Computational models of learning have proved largely successful in characterising potentialmechanism...
Computational models of learning have proved largely successful in characterizing potential mechanis...
Reinforcement learning algorithms have provided useful insights into human and an- imal learning and...
In this thesis we aim to improve generalisation in deep reinforcement learning. Generalisation is a ...
Abstract The complex behaviors we ultimately wish to understand are far from those currently used...
We argue that human inductive generalization is best explained in a Bayesian framework, rather than ...
In reinforcement learning (RL), an agent makes sequential decisions to maximise the reward it can ob...
Understanding the mechanisms behind learning and decision making under uncertainty remains an open c...