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  "documentTitle": "2022 Air Street Capital The State of AI Report 2022",
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  "authorName": "Air Street Capital",
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  "notes": "The slide validates a previous prediction using the IRIS model as a case study, supported by visual evidence of world model simulation and comparative performance metrics.",
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      "text": "They showed that their agent (dubbed IRIS) was sample efficient and surpassed human performance on 10 of the 26 games of Atari.",
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      "text": "Figure 5: Mean, median, and interquartile mean human normalized scores, computed with stratified bootstrapped confidence intervals [46]. 5 runs for IRIS and SimPLe, 100 runs for SPR, CURL, and DrQ [46].",
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      "text": "Figure 4: Pixel perfect predictions in Pong. The top row displays a test trajectory collected in the real environment. The bottom row depicts the reenactment of that trajectory inside the world model.",
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      "text": "In 2021, we predicted: “Transformers replace RNNs to learn world models with which RL agents surpass human performance in large and rich game environments.” Researchers from the University of Geneva used a GPT-like transformer to simulate the world environment. They showed that their agent (dubbed IRIS) was sample efficient and surpassed human performance on 10 of the 26 games of Atari. IRIS was notably the best method among the ones that don’t use lookahead search.",
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      "text": "2021 Prediction: transformers for learning in world models in reinforcement learning",
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