import json import requests from datasets import load_dataset import gradio as gr from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import HfApi, hf_hub_download from huggingface_hub.repocard import metadata_load import pandas as pd from utils import * block = gr.Blocks() # 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": "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": "Pixelcopter-PLE-v0", "rl_env": "Pixelcopter-PLE-v0", "video_link": "", "global": None } ] 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]) std_reward = float(parsed[1]) elif len(parsed)==1: #only mean reward mean_reward = float(parsed[0]) 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] #print(model_ids) return model_ids def get_model_dataframe(rl_env): # Get model ids associated with rl_env model_ids = get_model_ids(rl_env) #print(model_ids) 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"] = make_clickable_user(user_id) row["Model"] = make_clickable_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) print("DATA", data) ranked_dataframe = rank_dataframe(pd.DataFrame.from_records(data)) print("RANKED", ranked_dataframe) return ranked_dataframe def rank_dataframe(dataframe): #print("DATAFRAME", dataframe) dataframe = dataframe.sort_values(by=['Results'], 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 with block: gr.Markdown(f""" # 🏆 The Deep Reinforcement Learning Course Leaderboard 🏆 This is the leaderboard of trained agents during the Deep Reinforcement Learning Course. A free course from beginner to expert. Just choose which environment you trained your agent on and with Ctrl+F find your rank 🏆 **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** We use **lower bound result to sort the models: mean_reward - std_reward.** You **can click on the model's name** to be redirected to its model card which includes documentation. 🤖 You want to try to train your agents? Check the Hugging Face free Deep Reinforcement Learning Course 🤗 . You want to compare two agents? It's possible using this Spaces demo 👀 . 🔧 There is an **environment missing?** Please open an issue. For the RL course progress check out User Progress App """) #for rl_env in RL_ENVS: 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(): 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"]) with gr.Row(): data_run = gr.Button("Refresh") #print("rl_env", rl_env["rl_env"]) val = gr.Variable(value=[rl_env["rl_env"]]) data_run.click(get_model_dataframe, inputs=[val], outputs =rl_env["global"]) block.launch() def refresh_leaderboard(): """ Here we refresh the leaderboard: we update the rl_env["global"] for each rl_envs in rl_env """ for i in range(0, len(rl_envs)): rl_env = rl_envs[i] temp = get_model_dataframe(rl_env) rl_env["global"] = temp print("The leaderboard has been updated") scheduler = BackgroundScheduler() # Refresh every hour scheduler.add_job(func=refresh_leaderboard, trigger="interval", seconds=3600) scheduler.start()