Learning device, information processing system, learning method, and learning program

A learning method comprising:

• setting, by a computer, as a problem setting to be targeted in reinforcement learning, a model in which a policy for determining an action to be taken in an environmental state is associated with a Boltzmann distribution representing a probability distribution of a prescribed state, and a reward function for determining a reward obtainable from an environmental state and an action selected in the state is associated with a physical equation representing a physical quantity corresponding to an energy;
• estimating, by the computer, parameters of the physical equation by performing the reinforcement learning using training data including the state based on the set model;
• and
• detecting, by the computer, differences between previously estimated parameters of the physical equation and newly estimated parameters of the physical equation.

A non-transitory computer readable information recording medium storing a learning program, when executed by a processor, that performs a method for:

• setting, as a problem setting to be targeted in reinforcement learning, a model in which a policy for determining an action to be taken in an environmental state is associated with a Boltzmann distribution representing a probability distribution of a prescribed state, and a reward function for determining a reward obtainable from an environmental state and an action selected in the state is associated with a physical equation representing a physical quantity corresponding to an energy;
• estimating parameters of the physical equation by performing the reinforcement learning using training data including the state based on the set model;
• and
• detecting differences between previously estimated parameters of the physical equation and newly estimated parameters of the physical equation.