Add to ORKGInternational audienceReinforcement learning (RL) is a paradigm for learning sequential decision making tasks. However, typically the user must hand-tune exploration parameters for each different domain and/or algorithm that they are using. In this work, we present an algorithm called leo for learning these exploration strategies on-line. This algorithm makes use of bandit-type algorithms to adaptively select exploration strategies based on the rewards received when following them. We show empirically that this method performs well across a set of five domains. In contrast, for a given algorithm, no set of parameters is best across all domains. Our results demonstrate that the leo algorithm successfully learns the best exploration strategie...
International audienceOffline Reinforcement Learning (RL) aims at learning an optimal control from a...
We propose a new strategy for parallel reinforcement learning ; using this strategy, the optimal val...
AbstractReinforcement Learning (RL) is the study of programs that improve their performance by recei...
textReinforcement Learning (RL) offers a promising approach towards achieving the dream of autonomou...
Reinforcement learning (RL) is a machine learning paradigm where an agent learns to interact with an...
In this paper a reinforcement learning methodology for automatic online algorithm selection is intro...
Exploration is a critical component in reinforcement learning algorithms. Exploration exploitation t...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
Recent Reinforcement Learning (RL) algorithms, such as R-MAX, make (with high probability) only a sm...
This paper discusses parameter-based exploration methods for reinforcement learning. Parameter-based...
Exploration plays a fundamental role in any active learning system. This study evaluates the role of...
Reinforcement learning is a powerful approach for learning control policies that solve sequential de...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
This thesis describes reinforcement learning (RL) methods which can solve sequential decision makin...
Reinforcement learning (RL) has achieved impressive performance in various domains. However, most RL...
International audienceOffline Reinforcement Learning (RL) aims at learning an optimal control from a...
We propose a new strategy for parallel reinforcement learning ; using this strategy, the optimal val...
AbstractReinforcement Learning (RL) is the study of programs that improve their performance by recei...
textReinforcement Learning (RL) offers a promising approach towards achieving the dream of autonomou...
Reinforcement learning (RL) is a machine learning paradigm where an agent learns to interact with an...
In this paper a reinforcement learning methodology for automatic online algorithm selection is intro...
Exploration is a critical component in reinforcement learning algorithms. Exploration exploitation t...
Reinforcement learning (RL) focuses on an essential aspect of intelligent behavior – how an agent ca...
Recent Reinforcement Learning (RL) algorithms, such as R-MAX, make (with high probability) only a sm...
This paper discusses parameter-based exploration methods for reinforcement learning. Parameter-based...
Exploration plays a fundamental role in any active learning system. This study evaluates the role of...
Reinforcement learning is a powerful approach for learning control policies that solve sequential de...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
This thesis describes reinforcement learning (RL) methods which can solve sequential decision makin...
Reinforcement learning (RL) has achieved impressive performance in various domains. However, most RL...
International audienceOffline Reinforcement Learning (RL) aims at learning an optimal control from a...
We propose a new strategy for parallel reinforcement learning ; using this strategy, the optimal val...
AbstractReinforcement Learning (RL) is the study of programs that improve their performance by recei...