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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 apscheduler.schedulers.background import BackgroundScheduler |
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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 utils import * |
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block = gr.Blocks() |
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rl_envs = [ |
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{ |
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"rl_env_beautiful": "LunarLander-v2 🚀", |
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"rl_env": "LunarLander-v2", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "CartPole-v1", |
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"rl_env": "CartPole-v1", |
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"video_link": "https://huggingface.co/sb3/ppo-CartPole-v1/resolve/main/replay.mp4", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "FrozenLake-v1-4x4-no_slippery ❄️", |
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"rl_env": "FrozenLake-v1-4x4-no_slippery", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "FrozenLake-v1-8x8-no_slippery ❄️", |
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"rl_env": "FrozenLake-v1-8x8-no_slippery", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "FrozenLake-v1-4x4 ❄️", |
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"rl_env": "FrozenLake-v1-4x4", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "FrozenLake-v1-8x8 ❄️", |
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"rl_env": "FrozenLake-v1-8x8", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "Taxi-v3 🚖", |
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"rl_env": "Taxi-v3", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "CarRacing-v0 🏎️", |
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"rl_env": "CarRacing-v0", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "MountainCar-v0 ⛰️", |
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"rl_env": "MountainCar-v0", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "SpaceInvadersNoFrameskip-v4 👾", |
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"rl_env": "SpaceInvadersNoFrameskip-v4", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "PongNoFrameskip-v4 🎾", |
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"rl_env": "PongNoFrameskip-v4", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "BreakoutNoFrameskip-v4 🧱", |
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"rl_env": "BreakoutNoFrameskip-v4", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "QbertNoFrameskip-v4 🐦", |
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"rl_env": "QbertNoFrameskip-v4", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "BipedalWalker-v3", |
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"rl_env": "BipedalWalker-v3", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "Walker2DBulletEnv-v0", |
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"rl_env": "Walker2DBulletEnv-v0", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "AntBulletEnv-v0", |
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"rl_env": "AntBulletEnv-v0", |
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"video_link": "", |
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"global": None |
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}, |
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{ |
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"rl_env_beautiful": "HalfCheetahBulletEnv-v0", |
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"rl_env": "HalfCheetahBulletEnv-v0", |
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"video_link": "", |
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"global": None |
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} |
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] |
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def get_metadata(model_id): |
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try: |
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readme_path = hf_hub_download(model_id, filename="README.md") |
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return metadata_load(readme_path) |
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except requests.exceptions.HTTPError: |
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return None |
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def parse_metrics_accuracy(meta): |
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if "model-index" not in meta: |
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return None |
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result = meta["model-index"][0]["results"] |
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metrics = result[0]["metrics"] |
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accuracy = metrics[0]["value"] |
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return accuracy |
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def parse_rewards(accuracy): |
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default_std = -1000 |
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default_reward=-1000 |
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if accuracy != None: |
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accuracy = str(accuracy) |
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parsed = accuracy.split(' +/- ') |
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if len(parsed)>1: |
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mean_reward = float(parsed[0]) |
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std_reward = float(parsed[1]) |
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elif len(parsed)==1: |
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mean_reward = float(parsed[0]) |
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std_reward = float(0) |
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else: |
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mean_reward = float(default_std) |
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std_reward = float(default_reward) |
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else: |
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mean_reward = float(default_std) |
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std_reward = float(default_reward) |
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return mean_reward, std_reward |
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def get_model_ids(rl_env): |
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api = HfApi() |
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models = api.list_models(filter=rl_env) |
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model_ids = [x.modelId for x in models] |
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return model_ids |
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def get_model_dataframe(rl_env): |
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model_ids = get_model_ids(rl_env) |
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data = [] |
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for model_id in model_ids: |
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""" |
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readme_path = hf_hub_download(model_id, filename="README.md") |
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meta = metadata_load(readme_path) |
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""" |
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meta = get_metadata(model_id) |
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if meta is None: |
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continue |
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user_id = model_id.split('/')[0] |
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row = {} |
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row["User"] = make_clickable_user(user_id) |
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row["Model"] = make_clickable_model(model_id) |
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accuracy = parse_metrics_accuracy(meta) |
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mean_reward, std_reward = parse_rewards(accuracy) |
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mean_reward = mean_reward if not pd.isna(mean_reward) else 0 |
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std_reward = std_reward if not pd.isna(std_reward) else 0 |
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row["Results"] = mean_reward - std_reward |
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row["Mean Reward"] = mean_reward |
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row["Std Reward"] = std_reward |
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data.append(row) |
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print("DATA", data) |
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ranked_dataframe = rank_dataframe(pd.DataFrame.from_records(data)) |
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print("RANKED", ranked_dataframe) |
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return ranked_dataframe |
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def rank_dataframe(dataframe): |
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dataframe = dataframe.sort_values(by=['Results'], ascending=False) |
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if not 'Ranking' in dataframe.columns: |
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dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) |
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else: |
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dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] |
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return dataframe |
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with block: |
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gr.Markdown(f""" |
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# 🏆 The Deep Reinforcement Learning Course Leaderboard 🏆 |
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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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Just choose which environment you trained your agent on and with Ctrl+F find your rank 🏆 |
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**The leaderboard is updated every hour. If you don't find your model, go to the bottom of the page and click on the refresh button** |
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We use **lower bound result to sort the models: mean_reward - std_reward.** |
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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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🤖 You want to try to train your agents? <a href="https://huggingface.co/deep-rl-course/unit0/introduction?fw=pt" target="_blank"> Check the Hugging Face free Deep Reinforcement Learning Course 🤗 </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 👀 </a>. |
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🔧 There is an **environment missing?** Please open an issue. |
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""") |
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for i in range(0, len(rl_envs)): |
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rl_env = rl_envs[i] |
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with gr.TabItem(rl_env["rl_env_beautiful"]) as rl_tab: |
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with gr.Row(): |
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markdown = """ |
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# {name_leaderboard} |
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""".format(name_leaderboard = rl_env["rl_env_beautiful"], video_link = rl_env["video_link"]) |
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gr.Markdown(markdown) |
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with gr.Row(): |
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rl_env["global"] = gr.components.Dataframe(value= get_model_dataframe(rl_env["rl_env"]), headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"]) |
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with gr.Row(): |
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data_run = gr.Button("Refresh") |
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val = gr.Variable(value=[rl_env["rl_env"]]) |
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data_run.click(get_model_dataframe, inputs=[val], outputs =rl_env["global"]) |
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block.launch() |
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def refresh_leaderboard(): |
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""" |
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Here we refresh the leaderboard: |
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we update the rl_env["global"] for each rl_envs in rl_env |
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""" |
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for i in range(0, len(rl_envs)): |
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rl_env = rl_envs[i] |
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temp = get_model_dataframe(rl_env) |
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rl_env["global"] = temp |
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print("The leaderboard has been updated") |
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scheduler = BackgroundScheduler() |
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scheduler.add_job(func=refresh_leaderboard, trigger="interval", seconds=3600) |
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scheduler.start() |
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