Add to ORKGThis thesis studies the use of connectionist algorithms for solving reinforcement learning problems. Connectionist algorithms are inspired by the way information is processed by the brain: they rely on a large network of highly interconnected simple units, which process numerical information in a distributed and massively parallel way. Reinforcement learning is a computational theory that describes the interaction between an agent and an environment: it enables to precisely formalize goal-directed learning from interaction.We have considered three problems, with increasing complexity, and shown that they can be solved with connectionist algorithms: 1) Reinforcement learning in a small state space: we exploit a well-known algorithm in order ...
Reinforcement learning (RL) is a powerful machine learning framework to design algorithms that learn...
L'apprentissage par renforcement (reinforcement learning, RL) est un paradigme de l'apprentissage au...
Reinforcement learning is the area of machine learning concerned with learning which actions to exec...
This thesis studies the use of connectionist algorithms for solving reinforcement learning problems....
Colloque avec actes et comité de lecture. internationale.International audienceThis paper presents a...
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment ar...
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment ar...
The difficulties of learning in multilayered networks of computational units has limited the use of ...
This paper addresses automatic partitioning in complex reinforcement learning tasks with multiple ag...
This paper analyses the behaviour of a general class of learning automata algorithms for feedforward...
Artificial intelligence algorithms enable autonomous agents to perform sophisticated tasks with grea...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
An intelligent agent immerged in its environment must be able to both understand andinteract with th...
Reinforcement learning is the branch of machine learning characterized by learning from interaction ...
A novel modular connectionist architecture is presented in which the networks composing the architec...
Reinforcement learning (RL) is a powerful machine learning framework to design algorithms that learn...
L'apprentissage par renforcement (reinforcement learning, RL) est un paradigme de l'apprentissage au...
Reinforcement learning is the area of machine learning concerned with learning which actions to exec...
This thesis studies the use of connectionist algorithms for solving reinforcement learning problems....
Colloque avec actes et comité de lecture. internationale.International audienceThis paper presents a...
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment ar...
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment ar...
The difficulties of learning in multilayered networks of computational units has limited the use of ...
This paper addresses automatic partitioning in complex reinforcement learning tasks with multiple ag...
This paper analyses the behaviour of a general class of learning automata algorithms for feedforward...
Artificial intelligence algorithms enable autonomous agents to perform sophisticated tasks with grea...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
An intelligent agent immerged in its environment must be able to both understand andinteract with th...
Reinforcement learning is the branch of machine learning characterized by learning from interaction ...
A novel modular connectionist architecture is presented in which the networks composing the architec...
Reinforcement learning (RL) is a powerful machine learning framework to design algorithms that learn...
L'apprentissage par renforcement (reinforcement learning, RL) est un paradigme de l'apprentissage au...
Reinforcement learning is the area of machine learning concerned with learning which actions to exec...