Add to ORKGReinforcement learning (RL) has recently emerged as a generic yet powerful solution for learning complex decision-making policies, providing the key foundational underpinnings of recent successes in various domains, such as game playing and robotics. However, many state-of-the-art algorithms are data-hungry and computationally expensive, requiring large amounts of data to succeed. While this is possible for certain scenarios, in applications arising in social sciences and healthcare for example, where available data is sparse, this naturally can be costly or infeasible. With the surging interest in applying RL to broader domains, it is imperative to develop an informed view about the usage of data involved in its algorithmic design. This...
Reinforcement learning (RL) is a broad family of algorithms for training autonomous agents to collec...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
We study reinforcement learning (RL) in settings where observations are high-dimensional, but where ...
Reinforcement learning (RL) is a machine learning paradigm where an agent learns to interact with an...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
Different from classic Supervised Learning, Reinforcement Learning (RL), is fundamentally interactiv...
Abstract---Reinforcement learning (RL) has become more popular due to promising results in applicati...
Reinforcement learning with function approximation has recently achieved tremendous results in appli...
The impetus for exploration in reinforcement learning (RL) is decreasing uncertainty about the envir...
Sequentially making-decision abounds in real-world problems ranging from robots needing to interact ...
Reinforcement learning (RL) provides a formalism for learning-based control. By attempting to learn ...
Recently, the AlphaGo algorithm has managed to defeat the top level human player in the game of Go. ...
Reinforcement learning (RL) is a general framework for learning and evaluating intelligent behaviors...
Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. Th...
We propose a method for meta-learning reinforcement learning algorithms by searching over the space ...
Reinforcement learning (RL) is a broad family of algorithms for training autonomous agents to collec...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
We study reinforcement learning (RL) in settings where observations are high-dimensional, but where ...
Reinforcement learning (RL) is a machine learning paradigm where an agent learns to interact with an...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
Different from classic Supervised Learning, Reinforcement Learning (RL), is fundamentally interactiv...
Abstract---Reinforcement learning (RL) has become more popular due to promising results in applicati...
Reinforcement learning with function approximation has recently achieved tremendous results in appli...
The impetus for exploration in reinforcement learning (RL) is decreasing uncertainty about the envir...
Sequentially making-decision abounds in real-world problems ranging from robots needing to interact ...
Reinforcement learning (RL) provides a formalism for learning-based control. By attempting to learn ...
Recently, the AlphaGo algorithm has managed to defeat the top level human player in the game of Go. ...
Reinforcement learning (RL) is a general framework for learning and evaluating intelligent behaviors...
Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. Th...
We propose a method for meta-learning reinforcement learning algorithms by searching over the space ...
Reinforcement learning (RL) is a broad family of algorithms for training autonomous agents to collec...
While reinforcement learning (RL) algorithms have been successfully applied to a wide range of probl...
We study reinforcement learning (RL) in settings where observations are high-dimensional, but where ...