Add to ORKGAbstract. An algorithm based on Newton’s Method is proposed for ac-tion selection in continuous state- and action-space reinforcement learning without a policy network or discretization. The proposed method is val-idated on two benchmark problems: Cart-Pole and double Cart-Pole on which the proposed method achieves comparable or improved performance with less parameters to tune and in less training episodes than CACLA, which has previously been shown to outperform many other continuous state- and action-space reinforcement learning algorithms.
Here I apply three reinforcement learning methods to the full, continuous action, swing-up acrobot c...
Summarization: Reinforcement Learning methods for controlling stochastic processes typically assume ...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
An algorithm based on Newton's Method is proposed for action selection in continuous state- and acti...
An algorithm based on Newton’s Method is proposed for action selection in continuous state- and acti...
In this paper I investigate methods of applying reinforcement learning to continuous state- and acti...
Quite some research has been done on Reinforcement Learning in continuous environments, but the res...
The convergence properties for reinforcement learning approaches such as temporal dif-ferences and Q...
Here the Newton’s Method direct action selection approach to continuous action-space reinforcement l...
Continuous space reinforcement learning algorithms frequently fail to address the possibility of a c...
Here the Newton’s Method direct action selection approach to continuous action-space reinforcement l...
Summarization: The majority of learning algorithms available today focus on approximating the state ...
Reinforcement learning in the continuous state-space poses the problem of the inability to store the...
Abstract—This paper presents a new continuous action space for reinforcement learning (RL) with the ...
Here I apply three reinforcement learning methods to the full, continuous action, swing-up acrobot c...
Here I apply three reinforcement learning methods to the full, continuous action, swing-up acrobot c...
Summarization: Reinforcement Learning methods for controlling stochastic processes typically assume ...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
An algorithm based on Newton's Method is proposed for action selection in continuous state- and acti...
An algorithm based on Newton’s Method is proposed for action selection in continuous state- and acti...
In this paper I investigate methods of applying reinforcement learning to continuous state- and acti...
Quite some research has been done on Reinforcement Learning in continuous environments, but the res...
The convergence properties for reinforcement learning approaches such as temporal dif-ferences and Q...
Here the Newton’s Method direct action selection approach to continuous action-space reinforcement l...
Continuous space reinforcement learning algorithms frequently fail to address the possibility of a c...
Here the Newton’s Method direct action selection approach to continuous action-space reinforcement l...
Summarization: The majority of learning algorithms available today focus on approximating the state ...
Reinforcement learning in the continuous state-space poses the problem of the inability to store the...
Abstract—This paper presents a new continuous action space for reinforcement learning (RL) with the ...
Here I apply three reinforcement learning methods to the full, continuous action, swing-up acrobot c...
Here I apply three reinforcement learning methods to the full, continuous action, swing-up acrobot c...
Summarization: Reinforcement Learning methods for controlling stochastic processes typically assume ...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...