Read paper and watch video on World Model

[jiwoncpark]

Youtube video on the world model by Schmidhuber and Ha. The takeaway point is that, when trained inside a "dream" environment, i.e. an environment modeled by the MDN-RNN , the agent could learn a policy that had a higher score than when trained on "real" scenarios. The tau "temperature" parameter determines the degree of uncertainty. It seems the uncertainty helped the agent learn well. Also, training inside the simulated latent-space dream world is efficient! The world models were trained incrementally to simulate reality that is useful for transferring policies back to the real world.
Will this be useful for simulating PLAsTiCC data?