Abstract: An example embodiment includes a neural network unit to which a plurality of element values based on learning target data are input, and a learning unit that trains the neural network unit. The neural network unit has a plurality of learning cells each including a plurality of input nodes that perform predetermined weighting on each of the plurality of element values and an output node that sums the plurality of weighted element values and outputs the sum, and in accordance with an output value of each of the learning cells, the learning unit updates weighting coefficients of the plurality of input nodes of each of the learning cells or adds a new learning cell to the neural network unit.

Abstract: An action learning device includes an action candidate acquisition unit that extracts a plurality of possible action candidates based on situation information data representing an environment and a situation of a subject, a score acquisition unit that acquires a score that is an index representing an effect expected for a result caused by an action for each of the plurality of action candidates, an action selection unit that selects an action candidate having the largest score from the plurality of action candidates, and a score adjustment unit that adjusts a value of the score linked to the selected action candidate based on a result of the selected action candidate being performed on the environment.

Abstract: An example embodiment includes a neural network unit to which a plurality of element values based on learning target data are input, and a learning unit that trains the neural network unit. The neural network unit has a plurality of learning cells each including a plurality of input nodes that perform predetermined weighting on each of the plurality of element values and an output node that sums the plurality of weighted element values and outputs the sum, and in accordance with an output value of each of the learning cells, the learning unit updates weighting coefficients of the plurality of input nodes of each of the learning cells or adds a new learning cell to the neural network unit.