18 search results - page 2 / 4 » Global Reinforcement Learning in Neural Networks with Stocha... |
CEC
2007
IEEE
13 years 11 months ago
2007
IEEE
— Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advanta...
IJCNN
2000
IEEE
13 years 9 months ago
2000
IEEE
Since the classical work of D. O. Hebb [1] it has been assumed that synaptic plasticity solely depends on the activity of the pre- and the postsynaptic cell. Synapses influence th...
GECCO
2006
Springer
13 years 8 months ago
2006
Springer
In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
IWANN
2009
Springer
13 years 11 months ago
2009
Springer
We present an evolving neural network model in which synapses appear and disappear stochastically according to bio-inspired probabilities. These are in general nonlinear functions ...
NN
2006
Springer
13 years 5 months ago
2006
Springer
We discuss an important property called the asymptotic equipartition property on empirical sequences in reinforcement learning. This states that the typical set of empirical seque...