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We present a deep neural net-based controller trained by a model-free reinforcement learning (RL) al...
Robotic agents are becoming more prevalent in many settings, and their use in unstructured environme...
An important challenge for air–ground unmanned systems achieving autonomy is navigation, which is es...
This paper proposes a solution for the path following problem of a quadrotor vehicle based on deep r...
A deep reinforcement learning approach for solving the quadrotor path following and obstacle avoidan...
peer reviewedDeep learning techniques for motion control have recently been qualitatively improved, ...
The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by ...
In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable...
peer reviewedIn this paper we apply deep reinforcement learning techniques on a multicopter for lear...
Autonomous cars must be capable to operate in various conditions and learn from unforeseen scenario...
Reinforcement Learning (RL) is a learning paradigm where an agent learns a task by trial and error. ...
Deep learning techniques for motion control have recently been qualitatively improved, since the suc...
Deep Reinforcement Learning (DRL) is attracting increasing interest due to its ability to learn how ...
Deep Reinforcement Learning (DRL) is attracting increasing interest due to its ability to learn how ...
Reinforcement learning is a model-free technique to solve decision-making problems by learning the b...
We present a deep neural net-based controller trained by a model-free reinforcement learning (RL) al...
Robotic agents are becoming more prevalent in many settings, and their use in unstructured environme...
An important challenge for air–ground unmanned systems achieving autonomy is navigation, which is es...
This paper proposes a solution for the path following problem of a quadrotor vehicle based on deep r...
A deep reinforcement learning approach for solving the quadrotor path following and obstacle avoidan...
peer reviewedDeep learning techniques for motion control have recently been qualitatively improved, ...
The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by ...
In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable...
peer reviewedIn this paper we apply deep reinforcement learning techniques on a multicopter for lear...
Autonomous cars must be capable to operate in various conditions and learn from unforeseen scenario...
Reinforcement Learning (RL) is a learning paradigm where an agent learns a task by trial and error. ...
Deep learning techniques for motion control have recently been qualitatively improved, since the suc...
Deep Reinforcement Learning (DRL) is attracting increasing interest due to its ability to learn how ...
Deep Reinforcement Learning (DRL) is attracting increasing interest due to its ability to learn how ...
Reinforcement learning is a model-free technique to solve decision-making problems by learning the b...
We present a deep neural net-based controller trained by a model-free reinforcement learning (RL) al...
Robotic agents are becoming more prevalent in many settings, and their use in unstructured environme...
An important challenge for air–ground unmanned systems achieving autonomy is navigation, which is es...