Add to ORKGWe consider offline reinforcement learning (RL) methods in possibly nonstationary environments. Many existing RL algorithms in the literature rely on the stationarity assumption that requires the system transition and the reward function to be constant over time. However, the stationarity assumption is restrictive in practice and is likely to be violated in a number of applications, including traffic signal control, robotics and mobile health. In this paper, we develop a consistent procedure to test the nonstationarity of the optimal policy based on pre-collected historical data, without additional online data collection. Based on the proposed test, we further develop a sequential change point detection method that can be naturally coupled ...
Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learni...
Offline reinforcement learning algorithms still lack trust in practice due to the risk that the lear...
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
Reinforcement learning (RL) has emerged as a general-purpose technique for addressing problems invol...
Learning to act optimally in the complex world has long been a major goal in artificial intelligence...
Abstract. Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stati...
Recent research has turned to Reinforcement Learning (RL) to solve challenging decision problems, as...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
Recent research has turned to Reinforcement Learning (RL) to solve challenging decision problems, as...
Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learni...
Offline reinforcement learning algorithms still lack trust in practice due to the risk that the lear...
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
In most Reinforcement Learning (RL) studies, the considered task is assumed to be stationary, i.e., ...
Reinforcement learning (RL) has emerged as a general-purpose technique for addressing problems invol...
Learning to act optimally in the complex world has long been a major goal in artificial intelligence...
Abstract. Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stati...
Recent research has turned to Reinforcement Learning (RL) to solve challenging decision problems, as...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
Recent research has turned to Reinforcement Learning (RL) to solve challenging decision problems, as...
Offline reinforcement learning (RL) extends the paradigm of classical RL algorithms to purely learni...
Offline reinforcement learning algorithms still lack trust in practice due to the risk that the lear...
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some ...