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Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats

机译：基底神经节中强化​​学习的演员-批判模型：从自然大鼠到人工大鼠

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Since 1995, numerous Actor-Critic architectures for reinforcement learning have been proposed as models of dopamine-like reinforcement learning mechanisms in the rat's basal ganglia. However, these models were usually tested in different tasks, and it is then difficult to compare their efficiency for an autonomous animat. We present here the comparison of four architectures in an animat as it performs the same reward-seeking task. This will illustrate the consequences of different hypotheses about the management of different Actor sub-modules and Critic units, and their more or less autonomously determined coordination. We show that the classical method of coordination of modules by mixture of experts, depending on each module's performance, did not allow solving our task. Then we address the question of which principle should be applied efficiently to combine these units. Improvements for Critic modeling and accuracy of Actor-Critic models for a natural task are finally discussed in the perspective of our Psikharpax project—an artificial rat having to survive autonomously in unpredictable environments.
机译：自1995年以来，已经提出了许多用于强化学习的Actor-Critic体系结构，作为大鼠基底神经节中多巴胺样强化学习机制的模型。但是，这些模型通常在不同的任务中进行测试，因此很难比较它们对于自主动画的效率。我们在这里展示了动画中四种架构的比较，因为它执行相同的奖励任务。这将说明有关不同Actor子模块和Critic单元的管理的不同假设的后果，以及它们或多或少地自主确定的协调。我们证明了由专家的混合来协调模块的经典方法（取决于每个模块的性能）不能解决我们的任务。然后，我们讨论应该有效应用哪种原理来组合这些单元的问题。最后，从我们的Psikharpax项目的角度讨论了针对自然任务的Critic建模和Actor-Critic模型的准确性的改进，该项目是必须在无法预测的环境中自主生存的人工大鼠。

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