Learning device, information processing system, learning method, and learning program
AssigneeNEC Corporation
InventorRyota HIGA
Life of this patent
Estimating the term…
Abstract
A model setting unit 81 sets, 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. A parameter estimation unit 82 estimates parameters of the physical equation by performing the reinforcement learning using learning data including the state based on the set model.
Claims9
A learning device comprising a hardware processor configured to execute a software code to:
- set, 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;
- and
- estimate parameters of said physical equation by performing the reinforcement learning using learning data including said state based on said set model.
An information processing system comprising a hardware processor configured to execute a software code to:
- set, 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;
- estimate parameters of said physical equation by performing the reinforcement learning using learning data including said state based on said set model;
- estimate a state from an input action by using the estimated physical equation;
- and
- perform imitation learning based on said input action and the estimated state.
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;
- and
- estimating, by said computer, parameters of said physical equation by performing the reinforcement learning using learning data including said state based on said set model.
9-11. (canceled)
Dependencies are inferred from the claim text.