Cumulative Learning Through Intrinsic Reinforcements
نویسندگان
چکیده
Building artificial agents able to autonomously learn new skills and to easily adapt in different and complex environments is an important goal for robotics and machine learning. We propose that providing reinforcement learning artificial agents with a learning signal that resembles the characteristic of the phasic activations of dopaminergic neurons would be an advancement in the development of more autonomous and versatile systems. In particular, we suggest that the particular composition of such a signal, determined by both extrinsic and intrinsic reinforcements, would be suitable to improve the implementation of cumulative learning in artificial agents. To validate our hypothesis we performed experiments with a simulated robotic system that has to learn different skills to obtain extrinsic rewards. We compare different versions of the system varying the composition of the learning signal and we show that the only system able to reach high performance in the task is the one that implements the learning signal suggested by our hypothesis.
منابع مشابه
Biological cumulative learning through intrinsic motivations A simulated robotic study on the development of visually-guided reaching
This work aims to model the ability of biological organisms to achieve cumulative learning, i.e. learning increasingly more complex skills on the basis of simpler ones. In particular, we studied how a simulated kinematic robotic system composed of an arm and an eye can learn the ability to reach for an object on the basis of the ability to systematically look at the object, which, in our set-up...
متن کاملBiological cumulative learning through intrinsic motivations : A simulated robotic study of the development of visually-guided reaching
This work aims to model the ability of biological organisms to achieve cumulative learning, i.e. to learn increasingly more complex skills on the basis of simpler ones. In particular, we studied how a simulated kinematic robotic system composed of an arm and an eye can learn the ability to reach for an object on the basis of the ability to systematically look at the object, which, in our set-up...
متن کاملPhasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: A simulated robotic study
An important issue of recent neuroscientific research is to understand the functional role of the phasic release of dopamine in the striatum, and in particular its relation to reinforcement learning. The literature is split between two alternative hypotheses: one considers phasic dopamine as a reward prediction error similar to the computational TD-error, whose function is to guide an animal to...
متن کاملA bio-inspired learning signal for the cumulative learning of different skills
Building artificial agents able to autonomously learn new skills and to easily adapt in different and complex environments is an important goal for robotics and machine learning. We propose that providing artificial agents with a learning signal that resembles the characteristic of the phasic activations of dopaminergic neurons would be an advancement in the development of more autonomous and v...
متن کاملIntrinsic Motivation, Extrinsic Reinforcement, and Inequity
If a person who is intrinsically motivated to perform an activity begins to receive external reinforcement for the activity, what will happen to his intrinsic motivation? Previous studies and the present study indicate that money decreases intrinsic motivation, while verbal reinforcements tend to enhance intrinsic motivation. The beginning of a cognitive evaluation theory is discussed, and an a...
متن کامل