Add to ORKGThe field of reinforcement learning concerns the question of automated action se-lection given past experiences. As an agent moves through the state space, it must recognize which state choices are best in terms of allowing it to reach its goal. This is quantified with value functions, which evaluate a state and return the sum of rewards the agent can expect to receive from that state. Given a good value function, the agent can choose the actions which maximize this sum of rewards. Value functions are often chosen from a linear space defined by a set of features; this method offers a concise structure, low computational effort, and resistance to overfitting. However, because the number of features is small, this method depends heavily on th...
International audienceFeature discovery aims at finding the best representation of data. This is a v...
When applying reinforcement learning in domains with very large or continuous state spaces, the expe...
Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly...
The field of reinforcement learning concerns the question of automated action se-lection given past ...
A key element in the solution of reinforcement learning problems is the value function. The purpose ...
Reinforcement learning (RL) is generally considered as the machine learning answer to the optimal co...
In many practical reinforcement learning problems, the state space is too large to permit an exact r...
A key element in the solution of reinforcement learning problems is the value function. The purpose ...
To represent and learn a value function, one needs a set of features that facilitates the process by...
In value-based reinforcement learning (RL), unlike in supervised learning, the agent faces not a sin...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
Feature reinforcement learning was introduced five years ago as a principled and practical approach ...
Reinforcement learning (RL) is a computational framework for learning sequential decision strategies...
There are several reinforcement learning algorithms that yield ap-proximate solutions for the proble...
The field of Reinforcement Learning is concerned with teaching agents to take optimal decisions t...
International audienceFeature discovery aims at finding the best representation of data. This is a v...
When applying reinforcement learning in domains with very large or continuous state spaces, the expe...
Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly...
The field of reinforcement learning concerns the question of automated action se-lection given past ...
A key element in the solution of reinforcement learning problems is the value function. The purpose ...
Reinforcement learning (RL) is generally considered as the machine learning answer to the optimal co...
In many practical reinforcement learning problems, the state space is too large to permit an exact r...
A key element in the solution of reinforcement learning problems is the value function. The purpose ...
To represent and learn a value function, one needs a set of features that facilitates the process by...
In value-based reinforcement learning (RL), unlike in supervised learning, the agent faces not a sin...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
Feature reinforcement learning was introduced five years ago as a principled and practical approach ...
Reinforcement learning (RL) is a computational framework for learning sequential decision strategies...
There are several reinforcement learning algorithms that yield ap-proximate solutions for the proble...
The field of Reinforcement Learning is concerned with teaching agents to take optimal decisions t...
International audienceFeature discovery aims at finding the best representation of data. This is a v...
When applying reinforcement learning in domains with very large or continuous state spaces, the expe...
Model-based approaches to reinforcement learning exhibit low sample complexity while learning nearly...