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Forward propagation closed loop learning

机译：前向传播闭环学习

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For an autonomous agent, the inputs are the sensory data that inform the agent of the state of the world, and the outputs are their actions, which act on the world and consequently produce new sensory inputs. The agent only knows of its own actions via their effect on future inputs; therefore desired states, and error signals, are most naturally defined in terms of the inputs. Most machine learning algorithms, however, operate in terms of desired outputs. For example, backpropagation takes target output values and propagates the corresponding error backwards through the network in order to change the weights. In closed loop settings, it is far more obvious how to define desired sensory inputs than desired actions, however. To train a deep network using errors defined in the input space would call for an algorithm that can propagate those errors forwards through the network, from input layer to output layer, in much the same way that activations are propagated. In this article, we present a novel learning algorithm which performs such 'forward-propagation' of errors. We demonstrate its performance, first in a simple line follower and then in a 1st person shooter game.
机译：对于自治代理，输入是通知世界的代理人的感官数据，并且产出是他们的行为，这些行为是世界上的行动，从而产生新的感官输入。代理人只通过它们对未来投入的影响来了解自己的行动;因此，所需的状态和误差信号最自然地在输入方面定义。然而，大多数机器学习算法在所需输出方面运行。例如，BackPropagation采用目标输出值并通过网络向后传播相应的错误以便更改权重。在闭环设置中，如何定义所需的感觉输入比所需的动作更明显。为了使用输入空间中定义的错误训练深度网络，将呼叫可以通过网络转发的算法从输入层传播到输出层的算法，以与激活传播的方式相同的方式。在本文中，我们提出了一种新的学习算法，其执行错误的错误。我们展示了它的性能，首先在一个简单的线追随者中，然后在第一人称射击游戏中。

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