Any nonassociative reinforcement learning algorithm can be viewed as a method for performing function optimization through (possibly noise-corrupted) sampling of function values. We describe the results of simulations in which the optima of several deterministic functions studied by Ackley [1] were sought using variants of REINFORCE algorithms [19], [20]. Results obtained for certain of these algorithms compare favorably to the best results found by Ackley
This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem:...
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-ba...
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-ba...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
The application of reinforcement learning to problems with continuous domains requires representing ...
The application of reinforcement learning to problems with continuous domains requires representing ...
The application of reinforcement learning to problems with continuous domains requires representing ...
There are several reinforcement learning algorithms that yield ap-proximate solutions for the proble...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
Reinforcement learning is often done using parameterized function approximators to store value funct...
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to m...
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introdu...
Reinforcement learning is often done using parameterized function approximators to store value funct...
Reinforcement learning deals with the problem of sequential decision making in uncertain stochastic ...
This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem:...
This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem:...
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-ba...
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-ba...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
The application of reinforcement learning to problems with continuous domains requires representing ...
The application of reinforcement learning to problems with continuous domains requires representing ...
The application of reinforcement learning to problems with continuous domains requires representing ...
There are several reinforcement learning algorithms that yield ap-proximate solutions for the proble...
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to...
Reinforcement learning is often done using parameterized function approximators to store value funct...
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to m...
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introdu...
Reinforcement learning is often done using parameterized function approximators to store value funct...
Reinforcement learning deals with the problem of sequential decision making in uncertain stochastic ...
This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem:...
This thesis is mostly focused on reinforcement learning, which is viewed as an optimization problem:...
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-ba...
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-ba...
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