Add to ORKGAbstract—The reinforcement learning (RL) model has been very successful in behavioral sciences, artificial intelligence and neuroscience. Despite its fruitfulness in many simple situations, the RL model does not always cope well with real life situations involving a large space of possible world states or a large set of possible actions. We propose a modified version of the RL learning model. The benefit of this model is that the temporal difference prediction error can be used directly to update not only the value of the latest action of the learning agent, but the values of many possible future actions. An example application of this modified reinforcement learning infrastructure (MRLI) is presented for a customer behaviour in a complex s...
Humans and animals are capable of evaluating actions by considering their long-run future rewards th...
In recent years, there has been a growing interest in using rich representations such as relational...
When neural networks are used for approximating action-values of Reinforcement Learning (RL) agents,...
The reinforcement learning (RL) model has been very successful in behavioural sciences, artificial i...
Reinforcement learning (RL) has developed into a primary approach to learning control strate-gies fo...
Reinforcement Learning (RL) is a machine learning technique that enables artificial agents to learn ...
Reinforcement learning (RL) is an essential tool in design-ing autonomous systems, yet RL agents oft...
Learning to act optimally in the complex world has long been a major goal in artificial intelligence...
A key aspect of artificial intelligence is the ability to learn from experience. If examples of corr...
Reinforcement Learning (RL) is a popular method in machine learning. In RL, an agent learns a policy...
Reinforcement learning in complex environments may require supervision to prevent the agent from att...
Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection pol...
International audienceIn this paper we propose a method for solving reinforcement learning problems ...
Temporal difference (TD) learning methods (Sutton & Barto 1998) have become popular reinforcemen...
Humans and animals are capable of evaluating actions by considering their long-run future rewards th...
Humans and animals are capable of evaluating actions by considering their long-run future rewards th...
In recent years, there has been a growing interest in using rich representations such as relational...
When neural networks are used for approximating action-values of Reinforcement Learning (RL) agents,...
The reinforcement learning (RL) model has been very successful in behavioural sciences, artificial i...
Reinforcement learning (RL) has developed into a primary approach to learning control strate-gies fo...
Reinforcement Learning (RL) is a machine learning technique that enables artificial agents to learn ...
Reinforcement learning (RL) is an essential tool in design-ing autonomous systems, yet RL agents oft...
Learning to act optimally in the complex world has long been a major goal in artificial intelligence...
A key aspect of artificial intelligence is the ability to learn from experience. If examples of corr...
Reinforcement Learning (RL) is a popular method in machine learning. In RL, an agent learns a policy...
Reinforcement learning in complex environments may require supervision to prevent the agent from att...
Reinforcement Learning (RL) algorithms allow artificial agents to improve their action selection pol...
International audienceIn this paper we propose a method for solving reinforcement learning problems ...
Temporal difference (TD) learning methods (Sutton & Barto 1998) have become popular reinforcemen...
Humans and animals are capable of evaluating actions by considering their long-run future rewards th...
Humans and animals are capable of evaluating actions by considering their long-run future rewards th...
In recent years, there has been a growing interest in using rich representations such as relational...
When neural networks are used for approximating action-values of Reinforcement Learning (RL) agents,...