Add to ORKGhtmlabstractIn some stochastic environments the well-known reinforcement learning algorithm Q-learning performs very poorly. This poor performance is caused by large overestimations of action values, which result from a positive bias that is introduced because Q-learning uses the maximum action value as an approximation for the maximum expected action value. We introduce an alternative way to approximate the maximum expected value for any set of random variables. The obtained double estimator method is shown to sometimes underestimate rather than overestimate the maximum expected value. We apply the double estimator to Q-learning to construct Double Q-learning, a new off-policy reinforcement learning algorithm. We show the new algorithm con...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors...
Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to opti...
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...
Temporal-Difference off-policy algorithms are among the building blocks of reinforcement learning (R...
Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) pro...
The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overe...
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It...
Majority of off-policy reinforcement learning algorithms use overestimation bias control techniques....
In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation...
Value-based reinforcement-learning algorithms have shown strong performances in games, robotics, and...
How to get a good value estimation is one of the key problems in reinforcement learning (RL). Curren...
Q-learning is a very popular reinforcement learning algorithm being proven to converge to optimal po...
Q-learning is a very popular reinforcement learning algorithm be-ing proven to converge to optimal p...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors...
Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to opti...
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...
Temporal-Difference off-policy algorithms are among the building blocks of reinforcement learning (R...
Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) pro...
The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overe...
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It...
Majority of off-policy reinforcement learning algorithms use overestimation bias control techniques....
In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation...
Value-based reinforcement-learning algorithms have shown strong performances in games, robotics, and...
How to get a good value estimation is one of the key problems in reinforcement learning (RL). Curren...
Q-learning is a very popular reinforcement learning algorithm being proven to converge to optimal po...
Q-learning is a very popular reinforcement learning algorithm be-ing proven to converge to optimal p...
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL ...
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors...
Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to opti...