Add to ORKGAbstract. In this paper, we address an under-represented class of learning algorithms in the study of connectionism: reinforcement learning. We first introduce these classic methods in a new formalism which highlights the particularities of implementations such as Q-Learning, Q-Learning with Hamming distance, Q-Learning with statistical clustering and Dyna-Q. We then present in this formalism a neural implementation of reinforcement which clearly points out the advantages and the disadvantages of each approach. 1
[[abstract]]Recently, literature reported progresses on two reinforcement learning algorithms, AHC [...
In the past decade, research in neurocomputing has been divided into two relatively welldefined trac...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature and i...
Reinforcement learning relies on the association between a goal and a scalar signal, interpreted as ...
In the ¯eld of machine learning, reinforcement learning constitutes the idea of enabling machines to...
International audienceWe present the results of a research aimed at improving the Q-learning method ...
Reinforcement learning is an area of machine learning solving the problems that how to take actions ...
This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapti...
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncerta...
Neural Network models have received increased attention in the recent years. Aimed at achieving huma...
In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation...
This paper provides an overview of reinforcement learning (RL) and its potential for various applica...
Reinforcement learning algorithms comprise a class of learning algorithms for neural networks. Reinf...
[[abstract]]Recently, literature reported progresses on two reinforcement learning algorithms, AHC [...
In the past decade, research in neurocomputing has been divided into two relatively welldefined trac...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature and i...
Reinforcement learning relies on the association between a goal and a scalar signal, interpreted as ...
In the ¯eld of machine learning, reinforcement learning constitutes the idea of enabling machines to...
International audienceWe present the results of a research aimed at improving the Q-learning method ...
Reinforcement learning is an area of machine learning solving the problems that how to take actions ...
This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapti...
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncerta...
Neural Network models have received increased attention in the recent years. Aimed at achieving huma...
In Reinforcement learning, Q-learning is the best-known algorithm but it suffers from overestimation...
This paper provides an overview of reinforcement learning (RL) and its potential for various applica...
Reinforcement learning algorithms comprise a class of learning algorithms for neural networks. Reinf...
[[abstract]]Recently, literature reported progresses on two reinforcement learning algorithms, AHC [...
In the past decade, research in neurocomputing has been divided into two relatively welldefined trac...
Abstract—We have found a more general formulation of the REINFORCE learning principle which had been...