Add to ORKGReal-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete action spaces to facilitate the use of the many reinforcement learning algorithms that exist to find solutions for such MDPs. For many of these problems an underlying continuous action space can be assumed. We investigate the performance of the Cacla algorithm, which uses a continuous actor, on two such MDPs: the mountain car and the cart pole. We show that Cacla has clear advantages over discrete algorithms such as Q-learning and Sarsa, even though its continuous actions get rounded to actions in the same finite action space that may contain only a small number of actions. In particular, we show that Cacla retains much better performance when th...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Abstract — Real-world control problems are often modeled as Markov Decision Processes (MDPs) with di...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Quite some research has been done on Reinforcement Learning in continuous environments, but the res...
Recent research leverages results from the continuous-armed bandit literature to create a reinforcem...
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...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Summarization: The majority of learning algorithms available today focus on approximating the state ...
The convergence properties for reinforcement learning approaches such as temporal dif-ferences and Q...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Abstract — Real-world control problems are often modeled as Markov Decision Processes (MDPs) with di...
Real-world control problems are often modeled as Markov Decision Processes (MDPs) with discrete acti...
Quite some research has been done on Reinforcement Learning in continuous environments, but the res...
Recent research leverages results from the continuous-armed bandit literature to create a reinforcem...
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...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Summarization: The majority of learning algorithms available today focus on approximating the state ...
The convergence properties for reinforcement learning approaches such as temporal dif-ferences and Q...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...