Generative neural network systems for generating instruction sequences to control an agent performing a task

A method performed by one or more computers and for synthesizing programs for generating images, the method comprising:

• using a policy neural network to, at each of a sequence of time steps, select one or more actions for generating an image to be performed, wherein each action is a respective command to a graphics software configured to generate an image;
• controlling the graphics software using the respective commands to cause the graphics software to generate a graphics output comprising an image;
• processing the graphics output generated by the graphics software using a discriminator neural network to produce a discriminator output that indicates whether the graphics output is from a set of training data comprising images or was generated by the graphics software;
• and
• training the policy neural network using a reinforcement learning procedure according to a reward signal determined from the discriminator output;
• and
• training the discriminator neural network according to the discriminator output.

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for synthesizing programs for generating images, the operations comprising:

• using a policy neural network to, at each of a sequence of time steps, select one or more actions for generating an image to be performed, wherein each action is a respective command to a graphics software configured to generate an image;
• controlling the graphics software using the respective commands to cause the graphics software to generate a graphics output comprising an image;
• processing the graphics output generated by the graphics software using a discriminator neural network to produce a discriminator output that indicates whether the graphics output is from a set of training data comprising images or was generated by the graphics software;
• and
• training the policy neural network using a reinforcement learning procedure according to a reward signal determined from the discriminator output;
• and
• training the discriminator neural network according to the discriminator output.