Add to ORKGRecent research has turned to Reinforcement Learning (RL) to solve challenging decision problems, as an alternative to hand-tuned heuristics. RL can learn good policies without the need for modeling the environment's dynamics. Despite this promise, RL remains an impractical solution for many real-world systems problems. A particularly challenging case occurs when the environment changes over time, i.e. it exhibits non-stationarity. In this work, we characterize the challenges introduced by non-stationarity and develop a framework for addressing them to train RL agents in live systems. Such agents must explore and learn new environments, without hurting the system's performance, and remember them over time. To this end, our framework (1) ide...
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 this paper, we explore some issues associated with applying the Temporal Difference (TD) learning...
Recent research has turned to Reinforcement Learning (RL) to solve challenging decision problems, as...
Learning to act optimally in the complex world has long been a major goal in artificial intelligence...
Reinforcement learning (RL) has emerged as a general-purpose technique for addressing problems invol...
We consider offline reinforcement learning (RL) methods in possibly nonstationary environments. Many...
Reinforcement learning is a promising technique for learning agents to adapt their own strategies in...
Reinforcement learning (RL) agents empowered by deep neural networks have been considered a feasible...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
In this paper we introduce RL-CD, a method for solving reinforcement learning problems in non-statio...
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., ...
Abstract. Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stati...
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 this paper, we explore some issues associated with applying the Temporal Difference (TD) learning...
Recent research has turned to Reinforcement Learning (RL) to solve challenging decision problems, as...
Learning to act optimally in the complex world has long been a major goal in artificial intelligence...
Reinforcement learning (RL) has emerged as a general-purpose technique for addressing problems invol...
We consider offline reinforcement learning (RL) methods in possibly nonstationary environments. Many...
Reinforcement learning is a promising technique for learning agents to adapt their own strategies in...
Reinforcement learning (RL) agents empowered by deep neural networks have been considered a feasible...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
In this paper we introduce RL-CD, a method for solving reinforcement learning problems in non-statio...
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., ...
Abstract. Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stati...
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 this paper, we explore some issues associated with applying the Temporal Difference (TD) learning...