Add to ORKGThis thesis contains no material which has been accepted for the award of any other degree or diploma in any tertiary institution, and that, to my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the text of the thesis. Signed A number of reinforcement learning algorithms have been developed that are guaranteed to converge to an optimal solution for look-up tables. However, it has also been shown that these algorithms become unstable when used directly with a function approximation system. A new class of algorithms developed by Baird (1995) were created to handle the problem that direct algorithms have with function approximation systems. This thesi...
The application of reinforcement learning to problems with continuous domains requires representing ...
The application of reinforcement learning to problems with continuous domains requires representing ...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dyn...
A key aspect of artificial intelligence is the ability to learn from experience. If examples of corr...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and...
Combining reinforcement learning algorithms with function approximators in order to generalize over ...
International audienceAlong with the sharp increase in visibility of the field, the rate at which ne...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
The application of reinforcement learning to problems with continuous domains requires representing ...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and...
Any nonassociative reinforcement learning algorithm can be viewed as a method for performing functio...
The application of reinforcement learning to problems with continuous domains requires representing ...
The application of reinforcement learning to problems with continuous domains requires representing ...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dyn...
A key aspect of artificial intelligence is the ability to learn from experience. If examples of corr...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and...
Combining reinforcement learning algorithms with function approximators in order to generalize over ...
International audienceAlong with the sharp increase in visibility of the field, the rate at which ne...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...
The application of reinforcement learning to problems with continuous domains requires representing ...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and...
Any nonassociative reinforcement learning algorithm can be viewed as a method for performing functio...
The application of reinforcement learning to problems with continuous domains requires representing ...
The application of reinforcement learning to problems with continuous domains requires representing ...
Reinforcement learning is defined as the problem of an agent that learns to perform a certain task t...