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Moreover, we will perform an analysis of the neuronal computations underlying dopamine s role in reinforcement learning to understand how DA responses are generated at the circuit level.
These dopamine signals are believed to be important for reinforcement learning, the process by which an animal learns to perform actions that lead to reward.
Reinforcement learning and approximate dynamic programming for feedback control.
The range of subjects covered is fascinating, however, from game-theoretical applications to reinforcement learning, conservation of biodiversity and operations planning.
these moving averages are multiplied by some adjustment factors calibrated in the process of the reinforcement learning.
They proposed reinforcement learning for tuning of membership functions and developed two architectures ARIC and GARIC[51-53].
The Markov Decision Process has been an important tool for researchers in the held of Reinforcement Learning for years.
Abstract: Most reinforcement learning approaches consider Markov decision processes (MDPs) with a single criterion.