  Our effort is toward unifying GAN and DRL algorithms into a unifying AI model
(AGI or general-purpose AI or artificial general intelligence which has
general-purpose applications to: (A) offline learning (of stored data) like GAN
in (un/semi-/fully-)SL setting such as big data analytics (mining) and
visualization; (B) online learning (of real or simulated devices) like DRL in
RL setting (with/out environment reward) such as (real or simulated) robotics
and control; Our core proposal is adding an (generative/predictive) environment
model to the actor-critic (model-free) architecture which results in a
model-based actor-critic architecture with temporal-differencing (TD) error and
an episodic memory. The proposed AI model is similar to (model-free) DDPG and
therefore 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) of
independent simulated robotic and control task environments in OpenAI Gym and
Unity Agents. Our initial limited experiments show that DRL and GAN in
model-based actor-critic results in an incremental goal-driven intellignce
required to solve each task with similar performance to (model-free) DDPG. Our
future focus is to investigate the proposed AI model potential to: (A) unify
DRL field inside AI by producing competitive performance compared to the best
of model-based (PlaNet) and model-free (D4PG) approaches; (B) bridge the gap
between AI and robotics communities by solving the important problem of reward
engineering with learning the reward function by demonstration.
