Imitation learning by action shaping with antagonist reinforcement learning
AssigneeInternational Business Machines Corporation
InventorsTu-Hoa Pham, Giovanni De Magistris, Don Joven Ravoy Agravante, Ryuki Tachibana
Life of this patent
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Abstract
A computer-implemented method, computer program product, and computer processing system are provided for obtaining a plurality of bad demonstrations. The method includes reading, by a processor device, a protagonist environment. The method further includes training, by the processor device, a plurality of antagonist agents to fail a task by reinforcement learning using the protagonist environment. The method also includes collecting, by the processor device, the plurality of bad demonstrations by playing the trained antagonist agents on the protagonist environment.
Claims18
A computer-implemented method for obtaining a plurality of bad demonstrations, comprising:
- reading, by the processor device, a protagonist environment;
- training, by the processor device, a plurality of antagonist agents to fail a task by reinforcement learning using the protagonist environment to generate trained antagonist agents;
- collecting, by the processor device, the plurality of bad demonstrations by playing the trained antagonist agents on the protagonist environment;
- training a neural network with one or more of the plurality of bad demonstrations from the trained antagonist agents to generate a trained neural network;
- and
- controlling a vehicle system to control a vehicle to avoid a collision based on outputs from the trained neural network,
- wherein training the plurality of antagonist agents comprises:
- resetting a plurality of antagonist environments using the protagonist environment;
- and
- training the plurality of antagonist agents on a plurality of instances of each of the plurality of antagonist environments.
A computer program product for obtaining a plurality of bad demonstrations, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:
- reading, by the processor device, a protagonist environment;
- training, by the processor device, a plurality of antagonist agents to fail a task by reinforcement learning using the protagonist environment to generate trained antagonist agents;
- collecting, by the processor device, the plurality of bad demonstrations by playing the trained antagonist agents on the protagonist environment;
- training a neural network with one or more of the plurality of bad demonstrations from the trained antagonist agents to generate a trained neural network;
- and
- controlling a vehicle system to control a vehicle to avoid a collision based on outputs from the trained neural network,
- wherein training the plurality of antagonist agents comprises:
- resetting a plurality of antagonist environments using the protagonist environment;
- and
- training the plurality of antagonist agents on a plurality of instances of each of the plurality of antagonist environments.
A computer processing system for obtaining a plurality of bad demonstrations, comprising:
- a memory for storing program code;
- and
- a processor device operatively coupled to the memory for running the program code to read a protagonist environment;
- train a plurality of antagonist agents to fail a task by reinforcement learning using the protagonist environment to generate trained antagonist agents;
- collect the plurality of bad demonstrations by playing the trained antagonist agents on the protagonist environment;
- train a neural network with one or more of the plurality of bad demonstrations from the trained antagonist agents to generate a trained neural network;
- and
- control a vehicle system to control a vehicle to avoid a collision based on outputs from the trained neural network,
- wherein train the plurality of antagonist agents comprises:
- reset a plurality of antagonist environments using the protagonist environment;
- and
- train the plurality of antagonist agents on a plurality of instances of each of the plurality of antagonist environments.
Dependencies are inferred from the claim text.