nle VS Voyager

precisely, I really hope someone does Nethack next and leverages the learning environment that's already customized for it.

You should look at The Nethack Learning Environment.

There's quite a few open-source Reinforcement Learning challenges that you can explore with modest amounts of compute in order to build some experience training RL models, for example the Nethack Learning Environment, Atari, Minigrid, etc. For me personally, I had only worked in NLP / dialogue for years but got into RL by implementing Random Network Distillation models for NetHack. It's a fun area that definitely has its own unique challenges vs other domains. -AM

The NetHack Learning Environment\ (2 comments)

Source: Conversation with Bing, 12/10/2023 (1) Wes Roth - YouTube. https://www.youtube.com/@WesRoth. (2) I've set most of my videos to Public again - Community. https://community.openai.com/t/ive-set-most-of-my-videos-to-public-again/24535. (3) AI Updates: Meta Develops Mind-Reading AI System, OpenAI’s Q* Is Here .... https://www.windermeresun.com/2023/11/20/ai-updates-meta-develops-mind-reading-ai-system-openais-q-is-here-how-economy-will-work-after-agi/. (4) David Shapiro. https://www.daveshap.io/. (5) undefined. https://natural20.com/. (6) undefined. https://arxiv.org/abs/2305.16291. (7) undefined. https://twitter.com/DrJimFan/status/1. (8) undefined. https://voyager.minedojo.org/. (9) undefined. https://minedojo.org/. (10) undefined. https://www.youtube.com/@DavidShapiroAutomator/videos.

> AI cannot sustain itself trained on AI work.

This isn’t true. You can train LLMs entirely on synthetic data and get strong results. [0]

> If new languages, engines etc pop up it cannot synthesize new forms of coding without that code having existed in the first place.

You can describe the semantics to a LLM, have it generate code, tell it what went wrong (i.e. with compiler feedback), and then train on that. For an example of this workflow in a different context, see [1].

> And most importantly, it cannot fundamentally rationalize about what code does or how it functions.

Most competent LLMs can trivially describe what some code does and speculate on the reasoning behind it.

I don’t disagree that they’re flawed and imperfect, but I also do not think this is an unassailable state of affairs. They’re only going to get better from here.

[0]: https://arxiv.org/abs/2309.05463

[1]: https://voyager.minedojo.org/

In a way it is the same thing, agents are mostly an abstraction that make it easier to know what’s going on.

I think of agents more or less as python classes with a mixture of natural language and code functions. You design them to do something with information they produce, and to interface with other agents or “tools” in some way.

But all the agents can be the same language model under the hood, they are frames used to build different kinds of contexts.

And yes I think the idea is that emergent behaviour can be useful. This comes to mind

https://github.com/MineDojo/Voyager

But I think we are still a small ways off from being really smart about agents. My opinion is that we haven’t quite figured out what we are doing yet.

This k8s application deploys an instance of Voyager along with a Fabric Minecraft server with required fabric mods. It assumes you have a local deployment of a Large Language Model (LLM) with 4K-8K token context length with a compatible OpenAI API, including embeddings support.

In my opinion the most interesting Agents: Auto-GPT Github: https://github.com/Significant-Gravitas/Auto-GPT BabyAGI Github: https://github.com/yoheinakajima/babyagi Voyager Github: https://github.com/MineDojo/Voyager / Paper: https://arxiv.org/abs/2305.16291 I would also add: ChemCrow: Augmenting large-language models with chemistry tools Github: https://github.com/ur-whitelab/chemcrow-public/ Paper: https://arxiv.org/abs/2304.05376

Does this beat the voyager? I read about it and wondered what if we add a skill library to langchain/llamaindex agents. It could be the same vector store for storing static data but after each task is performed, the agent will evaluate and archive the recipe of steps to perform a new task. Next time when the agent is asked to perform a task, it can just look at the library to retrieve a recipe. Unlike traditional fine tuning, you dont update the model parameters, these recipes are much more interpretable and can be manually edited/inserted by humans. There may also be an automatic way to convert wikihow articles or youtube tutorials into recipes.

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Source.