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  1. Neural networks made easy (Part 40): Using Go-Explore on large amounts of data

    by , 06-29-2024 at 08:22 AM
    In the previous article "Neural networks made easy (Part 39): Go-Explore, a different approach to exploration", we familiarized ourselves with the Go-Explore algorithm and its ability to explore the environment.

    In this article, we will take a closer look at possible optimization methods for the Go-Explore algorithm to improve its efficiency over longer training periods.
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  2. Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method

    by , 06-25-2024 at 07:27 AM
    The Decision Transformer and all its modifications, which we discussed in recent articles, belong to the methods of Behavior Cloning (BC). We train models to repeat actions from "expert" trajectories depending on the state of the environment and the target outcomes. Thus, we teach the model to imitate the behavior of an expert in the current state of the environment in order to achieve the target.
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  3. Paul McCartney, Prince William, Tom Cruise: All the Celebs at Taylor Swift’s London Shows

    by , 06-25-2024 at 06:13 AM


    The singer’s three-day stint at Wembley Stadium drew famous fans, including a cameo onstage from her boyfriend Travis Kelce

    Taylor Swift invited several surprise guests on stage to perform during this weekend in London, but her Eras Tour run at Wembley Stadium also brought out a bunch of famous ...
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  4. New Zealand May Trade Surplus NZ$204 Million

    by , 06-24-2024 at 05:43 AM
    New Zealand posted a merchandise trade surplus of NZ$204 million in May, Statistics New Zealand said on Monday.

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  5. Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)

    by , 06-21-2024 at 07:27 AM
    PDT jointly learns an embedding space of future trajectory as well as a future prior conditioned only on past information.. By conditioning action prediction on the target future embedding, PDT is endowed with the ability to "reason over the future". This ability is naturally task-independent and can be generalized to different task specifications.

    To achieve efficient online fine-tuning in downstream tasks, you can easily adapt the framework to new conditions by associating each
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