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Our effort is toward unifying GAN and DRL algorithms into a unifying AI model(AGI or general-purpose AI or artificial general intelligence which hasgeneral-purpose applications to: (A) offline learning (of stored data) like GANin (un/semi-/fully-)SL setting such as big data analytics (mining) andvisualization; (B) online learning (of real or simulated devices) like DRL inRL setting (with/out environment reward) such as (real or simulated) roboticsand control; Our core proposal is adding an (generative/predictive) environmentmodel to the actor-critic (model-free) architecture which results in amodel-based actor-critic architecture with temporal-differencing (TD) error andan episodic memory. The proposed AI model is similar to (model-free) DDPG andtherefore it's called model-based DDPG. To evaluate it, we compare it with(model-free) DDPG by applying them both to a variety (wide range) ofindependent simulated robotic and control task environments in OpenAI Gym andUnity Agents. Our initial limited experiments show that DRL and GAN inmodel-based actor-critic results in an incremental goal-driven intelligncerequired to solve each task with similar performance to (model-free) DDPG. Ourfuture focus is to investigate the proposed AI model potential to: (A) unifyDRL field inside AI by producing competitive performance compared to the bestof model-based (PlaNet) and model-free (D4PG) approaches; (B) bridge the gapbetween AI and robotics communities by solving the important problem of rewardengineering with learning the reward function by demonstration.

Model-based actor-critic: GAN (model generator) + DRL (actor-critic) => AGI