Add to ORKGQuite some research has been done on Reinforcement Learning in continuous environments, but the research on problems where the actions can also be chosen from a continuous space is much more limited. We present a new class of algorithms named Continuous Actor Critic Learning Automaton (CACLA) that can handle continuous states and actions. The resulting algorithm is straightforward to implement. An experimental comparison is made between this algorithm and other algorithms that can handle continuous action spaces. These experiments show that CACLA performs much better than the other algorithms, especially when it is combined with a Gaussian exploration method
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...
Abstract. In this paper we address reinforcement learning problems with continuous state-action spac...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
In this article, we propose a new reinforcement learning (RL) method for a system having continuous ...
Abstract — Real-world control problems are often modeled as Markov Decision Processes (MDPs) with di...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...
Abstract. In this paper we address reinforcement learning problems with continuous state-action spac...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
In this article, we propose a new reinforcement learning (RL) method for a system having continuous ...
Abstract — Real-world control problems are often modeled as Markov Decision Processes (MDPs) with di...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
textabstractMany traditional reinforcement-learning algorithms have been designed for problems with ...
Reinforcement learning agents with artificial neural networks have previously been shown to acquire ...