Duplicate from huggingface-projects/Deep-Reinforcement-Learning-Leaderboard

.gitattributes

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.gitignore

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+__pycache__/*

README.md

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+---
+title: Deep Reinforcement Learning Leaderboard
+emoji: 🚀
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.11.0
+app_file: app.py
+pinned: false
+duplicated_from: huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+import json
+
+import requests
+
+from datasets import load_dataset
+import gradio as gr
+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": "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
+}      
+]
+
+
+
+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)
+    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 🏆
+
+    **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? <a href="https://huggingface.co/deep-rl-course/unit0/introduction?fw=pt" target="_blank"> Check the Hugging Face free Deep Reinforcement Learning Course 🤗 </a>.
+    
+    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>.
+    
+    🔧 There is an **environment missing?** Please open an issue.
+    """)
+    
+    #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()
+

utils.py

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+# Based on Omar Sanseviero work
+# Make model clickable link
+def make_clickable_model(model_name):
+    # remove user from model name
+    model_name_show = ' '.join(model_name.split('/')[1:])
+
+    link = "https://huggingface.co/" + model_name
+    return f'<a target="_blank" href="{link}">{model_name_show}</a>'
+
+# Make user clickable link
+def make_clickable_user(user_id):
+    link = "https://huggingface.co/" + user_id
+    return f'<a  target="_blank" href="{link}">{user_id}</a>'
+