Add to ORKG$Q$-learning with function approximation is one of the most empirically successful while theoretically mysterious reinforcement learning (RL) algorithms, and was identified in Sutton (1999) as one of the most important theoretical open problems in the RL community. Even in the basic linear function approximation setting, there are well-known divergent examples. In this work, we show that \textit{target network} and \textit{truncation} together are enough to provably stabilize $Q$-learning with linear function approximation, and we establish the finite-sample guarantees. The result implies an $O(\epsilon^{-2})$ sample complexity up to a function approximation error. Moreover, our results do not require strong assumptions or modifying the pro...
Q-learning is a very popular reinforcement learning algorithm being proven to converge to optimal po...
Reinforcement learning with function approximation has recently achieved tremendous results in appli...
This paper describes two novel on-policy reinforcement learning algorithms, named QV(lambda)-learni...
International audienceZap Q-learning is a recent class of reinforcement learning algorithms, motivat...
The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off...
Neural networks allow Q-learning reinforcement learning agents such as deep Q-networks (DQN) to appr...
Much of the recent successes in Deep Reinforcement Learning have been based on minimizing the square...
In this work we propose an approach for generalization in continuous domain Reinforcement Learning t...
We address the problem of computing the optimal Q-function in Markov decision prob-lems with infinit...
Q-learning is a reinforcement learning algorithm that has overestimation bias, because it learns the...
Q-learning is a reinforcement learning algorithm that has overestimation bias, because it learns the...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
We introduce the first algorithm for off-policy temporal-difference learning that is stable with lin...
Q-learning is a very popular reinforcement learning algorithm be-ing proven to converge to optimal p...
In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation...
Q-learning is a very popular reinforcement learning algorithm being proven to converge to optimal po...
Reinforcement learning with function approximation has recently achieved tremendous results in appli...
This paper describes two novel on-policy reinforcement learning algorithms, named QV(lambda)-learni...
International audienceZap Q-learning is a recent class of reinforcement learning algorithms, motivat...
The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off...
Neural networks allow Q-learning reinforcement learning agents such as deep Q-networks (DQN) to appr...
Much of the recent successes in Deep Reinforcement Learning have been based on minimizing the square...
In this work we propose an approach for generalization in continuous domain Reinforcement Learning t...
We address the problem of computing the optimal Q-function in Markov decision prob-lems with infinit...
Q-learning is a reinforcement learning algorithm that has overestimation bias, because it learns the...
Q-learning is a reinforcement learning algorithm that has overestimation bias, because it learns the...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
We introduce the first algorithm for off-policy temporal-difference learning that is stable with lin...
Q-learning is a very popular reinforcement learning algorithm be-ing proven to converge to optimal p...
In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation...
Q-learning is a very popular reinforcement learning algorithm being proven to converge to optimal po...
Reinforcement learning with function approximation has recently achieved tremendous results in appli...
This paper describes two novel on-policy reinforcement learning algorithms, named QV(lambda)-learni...