Credit Assignment Problem

Credit Assignment Problem-31
Figure 1 shows a summary diagram of the embedding of reinforcement learning depicting the links between the different fields.Red shows the most important theoretical and green the biological aspects related to RL, some of which will be described below (Wörgötter and Porr 2005).Note, the neuronal perspective of RL is in general indeed meant to address biological questions.

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We use cookies to offer you a better experience, personalize content, tailor advertising, provide social media features, and better understand the use of our services.(The use of the term reward is used here in a neutral fashion and does not imply any pleasure, hedonic impact or other psychological interpretations.) In general we are following Marr's approach (Marr et al 1982, later re-introduced by Gurney et al 2004) by introducing different levels: the algorithmic, the mechanistic and the implementation level.The best studied case is when RL can be formulated as class of Markov Decision Problems (MDP).In general there exist several ways for determining the optimal value function and/or the optimal policy.If we know the state transition function function T(s,a,s'), which describes the transition probability in going from state s to s' when performing action a, and if we know the reward function r(s,a), which determines how much reward is obtained at a state, then algorithms which are called model based algorithms can be devised.An RL agent learns from the consequences of its actions, rather than from being explicitly taught and it selects its actions on basis of its past experiences (exploitation) and also by new choices (exploration), which is essentially trial and error learning.The reinforcement signal that the RL-agent receives is a numerical reward, which encodes the success of an action's outcome, and the agent seeks to learn to select actions that maximize the accumulated reward over time.Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative: neurons can randomly introduce change, and use unspecific feedback signals to observe their effect on the cost and thus approximate their gradient. Each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide -- in this way it learns to learn.However, the convergence rate of such learning scales poorly with the number of involved neurons (e.g. We provide proof that our approach converges to the true gradient for certain classes of networks.To learn more or modify/prevent the use of cookies, see our Cookie Policy and Privacy Policy.Reinforcement learning (RL) is learning by interacting with an environment.

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