Add to ORKGWe propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration in high dimensions through posterior sampling but is usually computationally expensive. We address this limitation by introducing uncertainty only at the output layer of the network through a Bayesian Linear Regression (BLR) model, which can be trained with fast closed-form updates and its samples can be drawn efficiently through the Gaussian distribution. We apply our method to a wide range of Atari games in Arcade Learning Environments. Since BDQN carries out more efficient exploration, it is able to reach higher rewards substantially faster than a key baseline, double deep...
We address the problem of Bayesian reinforcement learning using efficient model-based online plannin...
Recently, deep neural networks have been capable of solving complex control tasks in certain challen...
In statistical dialogue management, the dialogue manager learns a policy that maps a belief state to...
We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based Reinforcement Learnin...
In the field of reinforcement learning, how to balance the relationship between exploration and expl...
© 2018, the Authors. Reinforcement learning (RL) aims to resolve the sequential decision-making unde...
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-lear...
Reinforcement learning has successfully been used in many applications and achieved prodigious perfo...
In the past decade, machine learning strategies centered on the use of Deep Neural Networks (DNNs) h...
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It...
Reinforcement learning algorithms based on Q-learning are driving Deep Reinforcement Learning (DRL) ...
Deep Q-learning Network (DQN) is a successful way which combines reinforcement learning with deep ne...
Reinforcement Learning is being used to solve various tasks. A Complex Environment is a recent probl...
International audienceCurrently, many applications in Machine Learning are based on define new model...
In combination with Deep Neural Networks (DNNs), several Reinforcement Learning (RL) algorithms such...
We address the problem of Bayesian reinforcement learning using efficient model-based online plannin...
Recently, deep neural networks have been capable of solving complex control tasks in certain challen...
In statistical dialogue management, the dialogue manager learns a policy that maps a belief state to...
We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based Reinforcement Learnin...
In the field of reinforcement learning, how to balance the relationship between exploration and expl...
© 2018, the Authors. Reinforcement learning (RL) aims to resolve the sequential decision-making unde...
We present a new algorithm that significantly improves the efficiency of exploration for deep Q-lear...
Reinforcement learning has successfully been used in many applications and achieved prodigious perfo...
In the past decade, machine learning strategies centered on the use of Deep Neural Networks (DNNs) h...
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It...
Reinforcement learning algorithms based on Q-learning are driving Deep Reinforcement Learning (DRL) ...
Deep Q-learning Network (DQN) is a successful way which combines reinforcement learning with deep ne...
Reinforcement Learning is being used to solve various tasks. A Complex Environment is a recent probl...
International audienceCurrently, many applications in Machine Learning are based on define new model...
In combination with Deep Neural Networks (DNNs), several Reinforcement Learning (RL) algorithms such...
We address the problem of Bayesian reinforcement learning using efficient model-based online plannin...
Recently, deep neural networks have been capable of solving complex control tasks in certain challen...
In statistical dialogue management, the dialogue manager learns a policy that maps a belief state to...