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About this paper
David Ha, Jürgen Schmidhuber
Researchers from Google Brain and NNAISENSE developed a modular agent architecture that learns an internal world model from raw pixels to facilitate policy evolution. It demonstrates successful transfer of policies trained entirely within the learned virtual environment to the actual environment, achieving state-of-the-art results on CarRacing-v0 and DoomTakeCover-v0.
Key insights
- Learning a probabilistic world model from high-dimensional visual inputs can extract useful spatio-temporal features, simplifying policy learning.
- Policies can be trained effectively and efficiently entirely within an environment simulated by the agent's own learned world model.
- Controlling the uncertainty (via a temperature parameter) in the internally generated environment is crucial to prevent policy exploitation and facilitate robust sim-to-real transfer.
Summary by alphaXiv.
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