  Advances in Deep Reinforcement Learning have led to agents that perform well
across a variety of sensory-motor domains. In this work, we study the setting
in which an agent must learn to generate programs for diverse scenes
conditioned on a given symbolic instruction. Final goals are specified to our
agent via images of the scenes. A symbolic instruction consistent with the goal
images is used as the conditioning input for our policies. Since a single
instruction corresponds to a diverse set of different but still consistent
end-goal images, the agent needs to learn to generate a distribution over
programs given an instruction. We demonstrate that with simple changes to the
reinforced adversarial learning objective, we can learn instruction conditioned
policies to achieve the corresponding diverse set of goals. Most importantly,
our agent's stochastic policy is shown to more accurately capture the diversity
in the goal distribution than a fixed pixel-based reward function baseline. We
demonstrate the efficacy of our approach on two domains: (1) drawing MNIST
digits with a paint software conditioned on instructions and (2) constructing
scenes in a 3D editor that satisfies a certain instruction.
