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We introduce Dreamer, an RL agent that solves long-horizon tasks from images purely by latent imagination inside a world model. Dreamer improves over existing methods across 20 tasks.
paper https://arxiv.org/pdf/1912.01603.pdf …
code https://github.com/google-research/dreamer …
Thread 
pic.twitter.com/K5DnooVIUH
Dreamer learns a world model from experience. Inside the compact latent space of the model, it predicts actions and state values. The policy is optimized efficiently by propagating analytic value gradients back through imagined trajectories.pic.twitter.com/61JMdSV76d
Naturally, the value function enables longsighted behavior and lets Dreamer be robust to the imagination horizon. This lets us solve new tasks that a policy without value function or online planning with PlaNet could not solve.pic.twitter.com/fee40JWADV
We evaluate Dreamer across 20 challenging visual control tasks with image inputs, where it exceeds previous methods in terms of final performance, sample-efficiency, and wall-clock time. Dreamer is also applicable to discrete actions and episodes with early termination.pic.twitter.com/JsfxKPoza3
Thanks to my advisors on the project: @Mo_Norouzi, Tim Lillicrap, and Jimmy Ba.
Let me know if you have any questions! 
https://danijar.com/dreamer
Did you try using local contexts for the contrastive loss as done in DIM and ST-DIM? You are right that the global contrastive loss cannot capture much information, but contrastive losses b/w local contexts seems to have quite an improvement (Table 1&2 in DIM, and Fig3 in ST-DIM)
I haven't but I think it's well worth trying
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