Showing papers on "Reinforcement learning published in 1981"

Associative search network: A reinforcement learning associative memory

Abstract: An associative memory system is presented which does not require a "teacher" to provide the desired associations. For each input key it conducts a search for the output pattern which optimizes an external payoff or reinforcement signal. The associative search network (ASN) combines pattern recognition and function optimization capabilities in a simple and effective way. We define the associative search problem, discuss conditions under which the associative search network is capable of solving it, and present results from computer simulations. The synthesis of sensory-motor control surfaces is discussed as an example of the associative search problem.

Associative search network - a reinforcement learning associative memory

Abstract: An associative memory system is presented which does not require a "teacher" to provide the desired associations For each input key it conducts a search for the output pattern which optimizes an external payoff or reinforcement signal The associative search network (ASN) combines pattern recognition and function optimization capabilities in a simple and effective way We define the associative search problem, discuss conditions under which the associative search network is capable of solving it, and present results from computer simulations The synthesis of sensory-motor control surfaces is discussed as an example of the associative search problem

Goal Seeking Components for Adaptive Intelligence: An Initial Assessment.

Abstract: : This report assesses the promise of a network approach to adaptive problem solving in which the network components themselves possess considerable adaptive power. We show that components designed with attention to the temporal aspects of reinforcement learning can acquire knowledge about feedback pathways in which they are embedded and can use this knowledge to seek their preferred inputs, thus combining pattern recognition, search, and control functions. A review of adaptive network research shows that networks of components having these capabilities have not been studied previously. We demonstrate that simple networks of these elements can solve types of problems that are beyond the capabilities of networks studied in the past. An associative memory is presented that retains the generalization capabilities and noise resistance of associative memories previously studied but does not require a 'teacher' to provide the desired associations. It conducts active, closed-loop searches for the most rewarding associations. We provide an example in whcih these searches are conducted through the system's external environment and an example in which they are conducted through an internal predictive model of that environment. The latter system is capable of a simple form of latent learning.