Arthur M. Collé arthurcolle

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meta_dsl_with_oorl.md Object-Oriented Reinforcement Learning in Mutable Ontologies with Self-Reflective Meta-DSL

Arthur M. Collé

Introduction

1.1. Motivation and Objectives

Reinforcement learning has made significant strides in enabling agents to learn complex behaviors through interaction with their environment. However, traditional approaches often struggle in open-ended, dynamic environments where the optimal behavior and relevant features may change over time.

	import openai
	import os
	import sys
	import inspect
	import ast
	import difflib

	# Initialize OpenAI client
	openai.api_type = 'openai'
	openai.api_key = os.getenv("OPENAI_API_KEY")

	[00:00:00.000 --> 00:00:04.320] Hi everyone. So today we are going to be continuing our Zero2Hero series
	[00:00:04.320 --> 00:00:10.640] and in particular today we are going to reproduce the GPT2 model, the 124 million version of it.
	[00:00:10.640 --> 00:00:17.440] So when OpenAI released GPT2, this was 2019 and they released it with this blog post.
	[00:00:17.440 --> 00:00:23.040] On top of that they released this paper and on top of that they released this code on GitHub,
	[00:00:23.040 --> 00:00:29.600] so OpenAI/GPT2. Now when we talk about reproducing GPT2, we have to be careful because in particular
	[00:00:29.600 --> 00:00:34.880] in this video we're going to be reproducing the 124 million parameter model. So the thing to
	[00:00:34.880 --> 00:00:41.040] realize is that there's always a miniseries when these releases are made, so there are the GPT2
	[00:00:41.040 --> 00:00:46.800] miniseries made up of models at different sizes and usually the biggest model is called the GPT2.
	[00:00:46.800

	import httpx
	import os
	import redis
	from fastapi import FastAPI, Request
	from pydantic import BaseModel, BaseConfig
	import httpx
	import asyncio

	API = os.getenv("FAKEMAIL", "https://1fef-216-158-152-64.ngrok-free.app")
	WEBHOOK_URL = os.getenv("WEBHOOK_PUBLIC_URL", "https://260d-2600-4040-4930-d500-ad7a-1287-fa48-5d5d.ngrok-free.app")