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import json |
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import requests |
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from datasets import load_dataset |
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import gradio as gr |
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from huggingface_hub import HfApi, hf_hub_download |
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from huggingface_hub.repocard import metadata_load |
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import pandas as pd |
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from matchmaking import * |
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from background_task import init_matchmaking |
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from apscheduler.schedulers.background import BackgroundScheduler |
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block = gr.Blocks() |
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env = [ |
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{ |
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"name": "Soccer", |
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"global": None, |
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}, |
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] |
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matchmaking = Matchmaking() |
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scheduler = BackgroundScheduler() |
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scheduler.add_job(func=init_matchmaking, trigger="interval", seconds=60) |
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scheduler.start() |
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def update_elos(): |
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matchmaking.read_history() |
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matchmaking.compute_elo() |
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matchmaking.save_elo_data() |
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def get_elo_data() -> pd.DataFrame: |
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data = pd.read_csv(f"env_elos/elo.csv") |
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return data |
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with block: |
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gr.Markdown(f""" |
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# ๐ The Deep Reinforcement Learning Course Leaderboard ๐ |
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This is the leaderboard of trained agents during the Deep Reinforcement Learning Course. A free course from beginner to expert. |
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This is the Soccer environment leaderboard, use Ctrl+F to find your rank ๐ |
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We use an ELO rating to sort the models. |
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You **can click on the model's name** to be redirected to its model card which includes documentation. |
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๐ค You want to try to train your agents? <a href="http://eepurl.com/ic5ZUD" target="_blank">Sign up to the Hugging Face free Deep Reinforcement Learning Course ๐ค </a>. |
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You want to compare two agents? <a href="https://huggingface.co/spaces/ThomasSimonini/Compare-Reinforcement-Learning-Agents" target="_blank">It's possible using this Spaces demo ๐ </a>. |
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๐ง There is an **environment missing?** Please open an issue. |
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""") |
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with gr.Blocks() as block: |
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gr.components.Dataframe( |
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value=get_elo_data(), |
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headers=["Ranking ๐", "User ๐ค", "Model id ๐ค", "ELO ๐", "Games played ๐ฎ"], |
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datatype=["number", "markdown", "markdown", "number", "number"] |
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) |
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block.launch() |
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