An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning
نویسندگان
چکیده
منابع مشابه
An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning
An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to...
متن کاملA Meta-learning Method Based on Temporal Difference Error
In general, meta-parameters in a reinforcement learning system, such as a learning rate and a discount rate, are empirically determined and fixed during learning. When an external environment is therefore changed, the sytem cannot adapt itself to the variation. Meanwhile, it is suggested that the biological brain might conduct reinforcement learning and adapt itself to the external environment ...
متن کاملAnalytical Mean Squared Error Curves in Temporal Difference Learning
Peter Dayan Brain and Cognitive Sciences E25-210, MIT Cambridge, MA 02139 [email protected] We have calculated analytical expressions for how the bias and variance of the estimators provided by various temporal difference value estimation algorithms change with offline updates over trials in absorbing Markov chains using lookup table representations. We illustrate classes of learning curve...
متن کاملAn Introduction to Temporal Difference Learning
Temporal Difference learning is one of the most used approaches for policy evaluation. It is a central part of solving reinforcement learning tasks. For deriving optimal control, policies have to be evaluated. This task requires value function approximation. At this point TD methods find application. The use of eligibility traces for backpropagation of updates as well as the bootstrapping of th...
متن کاملDual Temporal Difference Learning
Recently, researchers have investigated novel dual representations as a basis for dynamic programming and reinforcement learning algorithms. Although the convergence properties of classical dynamic programming algorithms have been established for dual representations, temporal difference learning algorithms have not yet been analyzed. In this paper, we study the convergence properties of tempor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PLoS Computational Biology
سال: 2011
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1001133