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| import os | |
| import json | |
| import requests | |
| import gradio as gr | |
| import pandas as pd | |
| from huggingface_hub import HfApi, hf_hub_download, snapshot_download | |
| from huggingface_hub.repocard import metadata_load | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| from tqdm.contrib.concurrent import thread_map | |
| from utils import * | |
| DATASET_REPO_URL = "https://huggingface.co/datasets/huggingface-projects/drlc-leaderboard-data" | |
| DATASET_REPO_ID = "huggingface-projects/drlc-leaderboard-data" | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| block = gr.Blocks() | |
| api = HfApi(token=HF_TOKEN) | |
| # Containing the data | |
| rl_envs = [ | |
| { | |
| "rl_env_beautiful": "LunarLander-v2 🚀", | |
| "rl_env": "LunarLander-v2", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "CartPole-v1", | |
| "rl_env": "CartPole-v1", | |
| "video_link": "https://huggingface.co/sb3/ppo-CartPole-v1/resolve/main/replay.mp4", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "FrozenLake-v1-4x4-no_slippery ❄️", | |
| "rl_env": "FrozenLake-v1-4x4-no_slippery", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "FrozenLake-v1-8x8-no_slippery ❄️", | |
| "rl_env": "FrozenLake-v1-8x8-no_slippery", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "FrozenLake-v1-4x4 ❄️", | |
| "rl_env": "FrozenLake-v1-4x4", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "FrozenLake-v1-8x8 ❄️", | |
| "rl_env": "FrozenLake-v1-8x8", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "Taxi-v3 🚖", | |
| "rl_env": "Taxi-v3", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "CarRacing-v0 🏎️", | |
| "rl_env": "CarRacing-v0", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "CarRacing-v2 🏎️", | |
| "rl_env": "CarRacing-v2", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "MountainCar-v0 ⛰️", | |
| "rl_env": "MountainCar-v0", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "SpaceInvadersNoFrameskip-v4 👾", | |
| "rl_env": "SpaceInvadersNoFrameskip-v4", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "PongNoFrameskip-v4 🎾", | |
| "rl_env": "PongNoFrameskip-v4", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "BreakoutNoFrameskip-v4 🧱", | |
| "rl_env": "BreakoutNoFrameskip-v4", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "QbertNoFrameskip-v4 🐦", | |
| "rl_env": "QbertNoFrameskip-v4", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "BipedalWalker-v3", | |
| "rl_env": "BipedalWalker-v3", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "Walker2DBulletEnv-v0", | |
| "rl_env": "Walker2DBulletEnv-v0", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "AntBulletEnv-v0", | |
| "rl_env": "AntBulletEnv-v0", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "HalfCheetahBulletEnv-v0", | |
| "rl_env": "HalfCheetahBulletEnv-v0", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "PandaReachDense-v2", | |
| "rl_env": "PandaReachDense-v2", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "PandaReachDense-v3", | |
| "rl_env": "PandaReachDense-v3", | |
| "video_link": "", | |
| "global": None | |
| }, | |
| { | |
| "rl_env_beautiful": "Pixelcopter-PLE-v0", | |
| "rl_env": "Pixelcopter-PLE-v0", | |
| "video_link": "", | |
| "global": None | |
| } | |
| ] | |
| def restart(): | |
| print("RESTART") | |
| api.restart_space(repo_id="huggingface-projects/Deep-Reinforcement-Learning-Leaderboard") | |
| def get_metadata(model_id): | |
| try: | |
| readme_path = hf_hub_download(model_id, filename="README.md") | |
| return metadata_load(readme_path) | |
| except requests.exceptions.HTTPError: | |
| # 404 README.md not found | |
| return None | |
| def parse_metrics_accuracy(meta): | |
| if "model-index" not in meta: | |
| return None | |
| result = meta["model-index"][0]["results"] | |
| metrics = result[0]["metrics"] | |
| accuracy = metrics[0]["value"] | |
| return accuracy | |
| # We keep the worst case episode | |
| def parse_rewards(accuracy): | |
| default_std = -1000 | |
| default_reward=-1000 | |
| if accuracy != None: | |
| accuracy = str(accuracy) | |
| parsed = accuracy.split('+/-') | |
| if len(parsed)>1: | |
| mean_reward = float(parsed[0].strip()) | |
| std_reward = float(parsed[1].strip()) | |
| elif len(parsed)==1: #only mean reward | |
| mean_reward = float(parsed[0].strip()) | |
| std_reward = float(0) | |
| else: | |
| mean_reward = float(default_std) | |
| std_reward = float(default_reward) | |
| else: | |
| mean_reward = float(default_std) | |
| std_reward = float(default_reward) | |
| return mean_reward, std_reward | |
| def get_model_ids(rl_env): | |
| api = HfApi() | |
| models = api.list_models(filter=rl_env) | |
| model_ids = [x.modelId for x in models] | |
| return model_ids | |
| # Parralelized version | |
| def update_leaderboard_dataset_parallel(rl_env, path): | |
| # Get model ids associated with rl_env | |
| model_ids = get_model_ids(rl_env) | |
| def process_model(model_id): | |
| meta = get_metadata(model_id) | |
| #LOADED_MODEL_METADATA[model_id] = meta if meta is not None else '' | |
| if meta is None: | |
| return None | |
| user_id = model_id.split('/')[0] | |
| row = {} | |
| row["User"] = user_id | |
| row["Model"] = model_id | |
| accuracy = parse_metrics_accuracy(meta) | |
| mean_reward, std_reward = parse_rewards(accuracy) | |
| mean_reward = mean_reward if not pd.isna(mean_reward) else 0 | |
| std_reward = std_reward if not pd.isna(std_reward) else 0 | |
| row["Results"] = mean_reward - std_reward | |
| row["Mean Reward"] = mean_reward | |
| row["Std Reward"] = std_reward | |
| return row | |
| data = list(thread_map(process_model, model_ids, desc="Processing models")) | |
| # Filter out None results (models with no metadata) | |
| data = [row for row in data if row is not None] | |
| ranked_dataframe = rank_dataframe(pd.DataFrame.from_records(data)) | |
| new_history = ranked_dataframe | |
| file_path = path + "/" + rl_env + ".csv" | |
| new_history.to_csv(file_path, index=False) | |
| return ranked_dataframe | |
| def update_leaderboard_dataset(rl_env, path): | |
| # Get model ids associated with rl_env | |
| model_ids = get_model_ids(rl_env) | |
| data = [] | |
| for model_id in model_ids: | |
| """ | |
| readme_path = hf_hub_download(model_id, filename="README.md") | |
| meta = metadata_load(readme_path) | |
| """ | |
| meta = get_metadata(model_id) | |
| #LOADED_MODEL_METADATA[model_id] = meta if meta is not None else '' | |
| if meta is None: | |
| continue | |
| user_id = model_id.split('/')[0] | |
| row = {} | |
| row["User"] = user_id | |
| row["Model"] = model_id | |
| accuracy = parse_metrics_accuracy(meta) | |
| mean_reward, std_reward = parse_rewards(accuracy) | |
| mean_reward = mean_reward if not pd.isna(mean_reward) else 0 | |
| std_reward = std_reward if not pd.isna(std_reward) else 0 | |
| row["Results"] = mean_reward - std_reward | |
| row["Mean Reward"] = mean_reward | |
| row["Std Reward"] = std_reward | |
| data.append(row) | |
| ranked_dataframe = rank_dataframe(pd.DataFrame.from_records(data)) | |
| new_history = ranked_dataframe | |
| file_path = path + "/" + rl_env + ".csv" | |
| new_history.to_csv(file_path, index=False) | |
| return ranked_dataframe | |
| def download_leaderboard_dataset(): | |
| path = snapshot_download(repo_id=DATASET_REPO_ID, repo_type="dataset") | |
| return path | |
| def get_data(rl_env, path) -> pd.DataFrame: | |
| """ | |
| Get data from rl_env | |
| :return: data as a pandas DataFrame | |
| """ | |
| csv_path = path + "/" + rl_env + ".csv" | |
| data = pd.read_csv(csv_path) | |
| for index, row in data.iterrows(): | |
| user_id = row["User"] | |
| data.loc[index, "User"] = make_clickable_user(user_id) | |
| model_id = row["Model"] | |
| data.loc[index, "Model"] = make_clickable_model(model_id) | |
| return data | |
| def get_data_no_html(rl_env, path) -> pd.DataFrame: | |
| """ | |
| Get data from rl_env | |
| :return: data as a pandas DataFrame | |
| """ | |
| csv_path = path + "/" + rl_env + ".csv" | |
| data = pd.read_csv(csv_path) | |
| return data | |
| def rank_dataframe(dataframe): | |
| dataframe = dataframe.sort_values(by=['Results', 'User', 'Model'], ascending=False) | |
| if not 'Ranking' in dataframe.columns: | |
| dataframe.insert(0, 'Ranking', [i for i in range(1,len(dataframe)+1)]) | |
| else: | |
| dataframe['Ranking'] = [i for i in range(1,len(dataframe)+1)] | |
| return dataframe | |
| def run_update_dataset(): | |
| path_ = download_leaderboard_dataset() | |
| for i in range(0, len(rl_envs)): | |
| rl_env = rl_envs[i] | |
| update_leaderboard_dataset_parallel(rl_env["rl_env"], path_) | |
| api.upload_folder( | |
| folder_path=path_, | |
| repo_id="huggingface-projects/drlc-leaderboard-data", | |
| repo_type="dataset", | |
| commit_message="Update dataset") | |
| def filter_data(rl_env, path, user_id): | |
| data_df = get_data_no_html(rl_env, path) | |
| models = [] | |
| models = data_df[data_df["User"] == user_id] | |
| for index, row in models.iterrows(): | |
| user_id = row["User"] | |
| models.loc[index, "User"] = make_clickable_user(user_id) | |
| model_id = row["Model"] | |
| models.loc[index, "Model"] = make_clickable_model(model_id) | |
| return models | |
| run_update_dataset() | |
| with block: | |
| gr.Markdown(f""" | |
| # 🏆 The Deep Reinforcement Learning Course Leaderboard 🏆 | |
| This is the leaderboard of trained agents during the <a href="https://huggingface.co/learn/deep-rl-course/unit0/introduction?fw=pt">Deep Reinforcement Learning Course</a>. A free course from beginner to expert. | |
| ### We only display the best 100 models | |
| If you want to **find yours, type your user id and click on Search my models.** | |
| You **can click on the model's name** to be redirected to its model card, including documentation. | |
| ### How are the results calculated? | |
| We use **lower bound result to sort the models: mean_reward - std_reward.** | |
| ### I can't find my model 😭 | |
| The leaderboard is **updated every two hours** if you can't find your models, just wait for the next update. | |
| ### The Deep RL Course | |
| 🤖 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>. | |
| 🔧 There is an **environment missing?** Please open an issue. | |
| """) | |
| path_ = download_leaderboard_dataset() | |
| for i in range(0, len(rl_envs)): | |
| rl_env = rl_envs[i] | |
| with gr.TabItem(rl_env["rl_env_beautiful"]) as rl_tab: | |
| with gr.Row(): | |
| markdown = """ | |
| # {name_leaderboard} | |
| """.format(name_leaderboard = rl_env["rl_env_beautiful"], video_link = rl_env["video_link"]) | |
| gr.Markdown(markdown) | |
| with gr.Row(): | |
| gr.Markdown(""" | |
| ## Search your models | |
| Simply type your user id to find your models | |
| """) | |
| with gr.Row(): | |
| user_id = gr.Textbox(label= "Your user id") | |
| search_btn = gr.Button("Search my models 🔎") | |
| reset_btn = gr.Button("Clear my search") | |
| env = gr.Variable(rl_env["rl_env"]) | |
| grpath = gr.Variable(path_) | |
| with gr.Row(): | |
| gr_dataframe = gr.components.Dataframe(value=get_data(rl_env["rl_env"], path_), headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"], row_count=(100, 'fixed')) | |
| with gr.Row(): | |
| #gr_search_dataframe = gr.components.Dataframe(headers=["Ranking 🏆", "User 🤗", "Model id 🤖", "Results", "Mean Reward", "Std Reward"], datatype=["number", "markdown", "markdown", "number", "number", "number"], visible=False) | |
| search_btn.click(fn=filter_data, inputs=[env, grpath, user_id], outputs=gr_dataframe, api_name="filter_data") | |
| with gr.Row(): | |
| search_btn.click(fn=filter_data, inputs=[env, grpath, user_id], outputs=gr_dataframe, api_name="filter_data") | |
| reset_btn.click(fn=get_data, inputs=[env, grpath], outputs=gr_dataframe, api_name="get_data") | |
| """ | |
| block.load( | |
| download_leaderboard_dataset, | |
| inputs=[], | |
| outputs=[ | |
| grpath | |
| ], | |
| ) | |
| """ | |
| scheduler = BackgroundScheduler() | |
| # Refresh every hour | |
| #scheduler.add_job(func=run_update_dataset, trigger="interval", seconds=3600) | |
| #scheduler.add_job(download_leaderboard_dataset, 'interval', seconds=3600) | |
| #scheduler.add_job(run_update_dataset, 'interval', seconds=3600) | |
| scheduler.add_job(restart, 'interval', seconds=7200) | |
| scheduler.start() | |
| block.launch() |