Deep RL Course documentation

Part 3: Load from Hub 🔽

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# Hands-on

Now that we studied the Q-Learning algorithm, let’s implement it from scratch and train our Q-Learning agent in two environments:

1. Frozen-Lake-v1 (non-slippery and slippery version) ☃️ : where our agent will need to go from the starting state (S) to the goal state (G) by walking only on frozen tiles (F) and avoiding holes (H).
2. An autonomous taxi 🚖 will need to learn to navigate a city to transport its passengers from point A to point B.

Thanks to a leaderboard, you’ll be able to compare your results with other classmates and exchange the best practices to improve your agent’s scores. Who will win the challenge for Unit 2?

If you don’t find your model, go to the bottom of the page and click on the refresh button.

To validate this hands-on for the certification process, you need to push your trained Taxi model to the Hub and get a result of >= 4.5.

To find your result, go to the leaderboard and find your model, the result = mean_reward - std of reward

And you can check your progress here 👉 https://huggingface.co/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course

To start the hands-on click on Open In Colab button 👇 :

# Unit 2: Q-Learning with FrozenLake-v1 ⛄ and Taxi-v3 🚕

In this notebook, you’ll code from scratch your first Reinforcement Learning agent playing FrozenLake ❄️ using Q-Learning, share it to the community, and experiment with different configurations.

⬇️ Here is an example of what you will achieve in just a couple of minutes. ⬇️

### 📚 RL-Library:

• Python and NumPy
• Gym

We’re constantly trying to improve our tutorials, so if you find some issues in this notebook, please open an issue on the GitHub Repo.

## Objectives of this notebook 🏆

At the end of the notebook, you will:

• Be able to use Gym, the environment library.
• Be able to code from scratch a Q-Learning agent.
• Be able to push your trained agent and the code to the Hub with a nice video replay and an evaluation score 🔥.

## Prerequisites 🏗️

Before diving into the notebook, you need to:

🔲 📚 Study Q-Learning by reading Unit 2 🤗

## A small recap of Q-Learning

• The Q-Learning is the RL algorithm that

• Trains Q-Function, an action-value function that contains, as internal memory, a Q-table that contains all the state-action pair values.

• Given a state and action, our Q-Function will search into its Q-table the corresponding value.

• When the training is done,we have an optimal Q-Function, so an optimal Q-Table.

• And if we have an optimal Q-function, we have an optimal policy,since we know for each state, what is the best action to take.

But, in the beginning, our Q-Table is useless since it gives arbitrary value for each state-action pair (most of the time we initialize the Q-Table to 0 values). But, as we’ll explore the environment and update our Q-Table it will give us better and better approximations

This is the Q-Learning pseudocode:

# Let's code our first Reinforcement Learning algorithm 🚀

## Install dependencies and create a virtual display 🔽

In the notebook, we’ll need to generate a replay video. To do so, with Colab, we need to have a virtual screen to render the environment (and thus record the frames).

Hence the following cell will install the libraries and create and run a virtual screen 🖥

We’ll install multiple ones:

• gym: Contains the FrozenLake-v1 ⛄ and Taxi-v3 🚕 environments. We use gym==0.24 since it contains a nice Taxi-v3 UI version.
• pygame: Used for the FrozenLake-v1 and Taxi-v3 UI.
• numpy: Used for handling our Q-table.

The Hugging Face Hub 🤗 works as a central place where anyone can share and explore models and datasets. It has versioning, metrics, visualizations and other features that will allow you to easily collaborate with others.

You can see here all the Deep RL models available (if they use Q Learning) 👉 https://huggingface.co/models?other=q-learning

pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit2/requirements-unit2.txt
sudo apt-get update
apt install python-opengl ffmpeg xvfb
pip3 install pyvirtualdisplay

To make sure the new installed libraries are used, sometimes it’s required to restart the notebook runtime. The next cell will force the runtime to crash, so you’ll need to connect again and run the code starting from here. Thanks for this trick, we will be able to run our virtual screen.

import os

os.kill(os.getpid(), 9)
# Virtual display
from pyvirtualdisplay import Display

virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()

## Import the packages 📦

In addition to the installed libraries, we also use:

• random: To generate random numbers (that will be useful for epsilon-greedy policy).
• imageio: To generate a replay video.
import numpy as np
import gym
import random
import imageio
import os

import pickle5 as pickle
from tqdm.notebook import tqdm

We’re now ready to code our Q-Learning algorithm 🔥

# Part 1: Frozen Lake ⛄ (non slippery version)

## Create and understand FrozenLake environment ⛄

💡 A good habit when you start to use an environment is to check its documentation

We’re going to train our Q-Learning agent to navigate from the starting state (S) to the goal state (G) by walking only on frozen tiles (F) and avoid holes (H).

We can have two sizes of environment:

• map_name="4x4": a 4x4 grid version
• map_name="8x8": a 8x8 grid version

The environment has two modes:

• is_slippery=False: The agent always moves in the intended direction due to the non-slippery nature of the frozen lake (deterministic).
• is_slippery=True: The agent may not always move in the intended direction due to the slippery nature of the frozen lake (stochastic).

For now let’s keep it simple with the 4x4 map and non-slippery

# Create the FrozenLake-v1 environment using 4x4 map and non-slippery version
env = gym.make()  # TODO use the correct parameters

### Solution

env = gym.make("FrozenLake-v1", map_name="4x4", is_slippery=False)

You can create your own custom grid like this:

desc=["SFFF", "FHFH", "FFFH", "HFFG"]
gym.make('FrozenLake-v1', desc=desc, is_slippery=True)

but we’ll use the default environment for now.

### Let's see what the Environment looks like:

# We create our environment with gym.make("<name_of_the_environment>")- is_slippery=False: The agent always moves in the intended direction due to the non-slippery nature of the frozen lake (deterministic).
print("_____OBSERVATION SPACE_____ \n")
print("Observation Space", env.observation_space)
print("Sample observation", env.observation_space.sample())  # Get a random observation

We see with Observation Space Shape Discrete(16) that the observation is an integer representing the agent’s current position as current_row * nrows + current_col (where both the row and col start at 0).

For example, the goal position in the 4x4 map can be calculated as follows: 3 * 4 + 3 = 15. The number of possible observations is dependent on the size of the map. For example, the 4x4 map has 16 possible observations.

For instance, this is what state = 0 looks like:

print("\n _____ACTION SPACE_____ \n")
print("Action Space Shape", env.action_space.n)
print("Action Space Sample", env.action_space.sample())  # Take a random action

The action space (the set of possible actions the agent can take) is discrete with 4 actions available 🎮:

• 0: GO LEFT
• 1: GO DOWN
• 2: GO RIGHT
• 3: GO UP

Reward function 💰:

• Reach goal: +1
• Reach hole: 0
• Reach frozen: 0

## Create and Initialize the Q-table 🗄️

(👀 Step 1 of the pseudocode)

It’s time to initialize our Q-table! To know how many rows (states) and columns (actions) to use, we need to know the action and observation space. We already know their values from before, but we’ll want to obtain them programmatically so that our algorithm generalizes for different environments. Gym provides us a way to do that: env.action_space.n and env.observation_space.n

state_space =
print("There are ", state_space, " possible states")

action_space =
print("There are ", action_space, " possible actions")
# Let's create our Qtable of size (state_space, action_space) and initialized each values at 0 using np.zeros. np.zeros needs a tuple (a,b)

def initialize_q_table(state_space, action_space):
Qtable =
return Qtable
Qtable_frozenlake = initialize_q_table(state_space, action_space)

### Solution

state_space = env.observation_space.n
print("There are ", state_space, " possible states")

action_space = env.action_space.n
print("There are ", action_space, " possible actions")
# Let's create our Qtable of size (state_space, action_space) and initialized each values at 0 using np.zeros
def initialize_q_table(state_space, action_space):
Qtable = np.zeros((state_space, action_space))
return Qtable
Qtable_frozenlake = initialize_q_table(state_space, action_space)

## Define the greedy policy 🤖

Remember we have two policies since Q-Learning is an **off-policy** algorithm. This means we're using a **different policy for acting and updating the value function**.
• Epsilon-greedy policy (acting policy)
• Greedy-policy (updating policy)

Greedy policy will also be the final policy we’ll have when the Q-learning agent will be trained. The greedy policy is used to select an action from the Q-table.

def greedy_policy(Qtable, state):
# Exploitation: take the action with the highest state, action value
action =

return action

#### Solution

def greedy_policy(Qtable, state):
# Exploitation: take the action with the highest state, action value
action = np.argmax(Qtable[state][:])

return action

##Define the epsilon-greedy policy 🤖

Epsilon-greedy is the training policy that handles the exploration/exploitation trade-off.

The idea with epsilon-greedy:

• With probability 1 - ɛ : we do exploitation (i.e. our agent selects the action with the highest state-action pair value).

• With probability ɛ: we do exploration (trying random action).

And as the training goes, we progressively reduce the epsilon value since we will need less and less exploration and more exploitation.

def epsilon_greedy_policy(Qtable, state, epsilon):
# Randomly generate a number between 0 and 1
random_num =
# if random_num > greater than epsilon --> exploitation
if random_num > epsilon:
# Take the action with the highest value given a state
# np.argmax can be useful here
action =
# else --> exploration
else:
action = # Take a random action

return action

#### Solution

def epsilon_greedy_policy(Qtable, state, epsilon):
# Randomly generate a number between 0 and 1
random_int = random.uniform(0, 1)
# if random_int > greater than epsilon --> exploitation
if random_int > epsilon:
# Take the action with the highest value given a state
# np.argmax can be useful here
action = greedy_policy(Qtable, state)
# else --> exploration
else:
action = env.action_space.sample()

return action

## Define the hyperparameters ⚙️

The exploration related hyperparameters are some of the most important ones.
• We need to make sure that our agent explores enough of the state space to learn a good value approximation. To do that, we need to have progressive decay of the epsilon.
• If you decrease epsilon too fast (too high decay_rate), you take the risk that your agent will be stuck, since your agent didn’t explore enough of the state space and hence can’t solve the problem.
# Training parameters
n_training_episodes = 10000  # Total training episodes
learning_rate = 0.7  # Learning rate

# Evaluation parameters
n_eval_episodes = 100  # Total number of test episodes

# Environment parameters
env_id = "FrozenLake-v1"  # Name of the environment
max_steps = 99  # Max steps per episode
gamma = 0.95  # Discounting rate
eval_seed = []  # The evaluation seed of the environment

# Exploration parameters
max_epsilon = 1.0  # Exploration probability at start
min_epsilon = 0.05  # Minimum exploration probability
decay_rate = 0.0005  # Exponential decay rate for exploration prob

## Create the training loop method

The training loop goes like this:

For episode in the total of training episodes:

Reduce epsilon (since we need less and less exploration)
Reset the environment

For step in max timesteps:
Choose the action At using epsilon greedy policy
Take the action (a) and observe the outcome state(s') and reward (r)
Update the Q-value Q(s,a) using Bellman equation Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]
If done, finish the episode
Our next state is the new state
def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable):
for episode in range(n_training_episodes):
# Reduce epsilon (because we need less and less exploration)
epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)
# Reset the environment
state = env.reset()
step = 0
done = False

# repeat
for step in range(max_steps):
# Choose the action At using epsilon greedy policy
action =

# Take action At and observe Rt+1 and St+1
# Take the action (a) and observe the outcome state(s') and reward (r)
new_state, reward, done, info =

# Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]
Qtable[state][action] =

# If done, finish the episode
if done:
break

# Our next state is the new state
state = new_state
return Qtable

#### Solution

def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable):
for episode in tqdm(range(n_training_episodes)):
# Reduce epsilon (because we need less and less exploration)
epsilon = min_epsilon + (max_epsilon - min_epsilon) * np.exp(-decay_rate * episode)
# Reset the environment
state = env.reset()
step = 0
done = False

# repeat
for step in range(max_steps):
# Choose the action At using epsilon greedy policy
action = epsilon_greedy_policy(Qtable, state, epsilon)

# Take action At and observe Rt+1 and St+1
# Take the action (a) and observe the outcome state(s') and reward (r)
new_state, reward, done, info = env.step(action)

# Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]
Qtable[state][action] = Qtable[state][action] + learning_rate * (
reward + gamma * np.max(Qtable[new_state]) - Qtable[state][action]
)

# If done, finish the episode
if done:
break

# Our next state is the new state
state = new_state
return Qtable

## Train the Q-Learning agent 🏃

Qtable_frozenlake = train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_frozenlake)

## Let's see what our Q-Learning table looks like now 👀

Qtable_frozenlake

## The evaluation method 📝

• We defined the evaluation method that we’re going to use to test our Q-Learning agent.
def evaluate_agent(env, max_steps, n_eval_episodes, Q, seed):
"""
Evaluate the agent for n_eval_episodes episodes and returns average reward and std of reward.
:param env: The evaluation environment
:param n_eval_episodes: Number of episode to evaluate the agent
:param Q: The Q-table
:param seed: The evaluation seed array (for taxi-v3)
"""
episode_rewards = []
for episode in tqdm(range(n_eval_episodes)):
if seed:
state = env.reset(seed=seed[episode])
else:
state = env.reset()
step = 0
done = False
total_rewards_ep = 0

for step in range(max_steps):
# Take the action (index) that have the maximum expected future reward given that state
action = greedy_policy(Q, state)
new_state, reward, done, info = env.step(action)
total_rewards_ep += reward

if done:
break
state = new_state
episode_rewards.append(total_rewards_ep)
mean_reward = np.mean(episode_rewards)
std_reward = np.std(episode_rewards)

return mean_reward, std_reward

## Evaluate our Q-Learning agent 📈

• Usually, you should have a mean reward of 1.0
• The environment is relatively easy since the state space is really small (16). What you can try to do is to replace it with the slippery version, which introduces stochasticity, making the environment more complex.
# Evaluate our Agent
mean_reward, std_reward = evaluate_agent(env, max_steps, n_eval_episodes, Qtable_frozenlake, eval_seed)
print(f"Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}")

## Publish our trained model to the Hub 🔥

Now that we saw good results after the training, we can publish our trained model to the Hub 🤗 with one line of code.

Here’s an example of a Model Card:

Under the hood, the Hub uses git-based repositories (don’t worry if you don’t know what git is), which means you can update the model with new versions as you experiment and improve your agent.

#### Do not modify this code

from huggingface_hub import HfApi, snapshot_download

from pathlib import Path
import datetime
import json
def record_video(env, Qtable, out_directory, fps=1):
"""
Generate a replay video of the agent
:param env
:param Qtable: Qtable of our agent
:param out_directory
:param fps: how many frame per seconds (with taxi-v3 and frozenlake-v1 we use 1)
"""
images = []
done = False
state = env.reset(seed=random.randint(0, 500))
img = env.render(mode="rgb_array")
images.append(img)
while not done:
# Take the action (index) that have the maximum expected future reward given that state
action = np.argmax(Qtable[state][:])
state, reward, done, info = env.step(action)  # We directly put next_state = state for recording logic
img = env.render(mode="rgb_array")
images.append(img)
imageio.mimsave(out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps)
def push_to_hub(repo_id, model, env, video_fps=1, local_repo_path="hub"):
"""
Evaluate, Generate a video and Upload a model to Hugging Face Hub.
This method does the complete pipeline:
- It evaluates the model
- It generates the model card
- It generates a replay video of the agent
- It pushes everything to the Hub

:param repo_id: repo_id: id of the model repository from the Hugging Face Hub
:param env
:param video_fps: how many frame per seconds to record our video replay
(with taxi-v3 and frozenlake-v1 we use 1)
:param local_repo_path: where the local repository is
"""
_, repo_name = repo_id.split("/")

eval_env = env
api = HfApi()

# Step 1: Create the repo
repo_url = api.create_repo(
repo_id=repo_id,
exist_ok=True,
)

# Step 3: Save the model
if env.spec.kwargs.get("map_name"):
model["map_name"] = env.spec.kwargs.get("map_name")
if env.spec.kwargs.get("is_slippery", "") == False:
model["slippery"] = False

# Pickle the model
with open((repo_local_path) / "q-learning.pkl", "wb") as f:
pickle.dump(model, f)

# Step 4: Evaluate the model and build JSON with evaluation metrics
mean_reward, std_reward = evaluate_agent(
eval_env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]
)

evaluate_data = {
"env_id": model["env_id"],
"mean_reward": mean_reward,
"n_eval_episodes": model["n_eval_episodes"],
"eval_datetime": datetime.datetime.now().isoformat(),
}

# Write a JSON file called "results.json" that will contain the
# evaluation results
with open(repo_local_path / "results.json", "w") as outfile:
json.dump(evaluate_data, outfile)

# Step 5: Create the model card
env_name = model["env_id"]
if env.spec.kwargs.get("map_name"):
env_name += "-" + env.spec.kwargs.get("map_name")

if env.spec.kwargs.get("is_slippery", "") == False:
env_name += "-" + "no_slippery"

metadata["tags"] = [env_name, "q-learning", "reinforcement-learning", "custom-implementation"]

model_pretty_name=repo_name,
metrics_pretty_name="mean_reward",
metrics_id="mean_reward",
metrics_value=f"{mean_reward:.2f} +/- {std_reward:.2f}",
dataset_pretty_name=env_name,
dataset_id=env_name,
)

# Merges both dictionaries

model_card = f"""
# **Q-Learning** Agent playing1 **{env_id}**
This is a trained model of a **Q-Learning** agent playing **{env_id}** .

## Usage

python

# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])

"""

evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])

else:

# Step 6: Record a video
video_path = repo_local_path / "replay.mp4"
record_video(env, model["qtable"], video_path, video_fps)

# Step 7. Push everything to the Hub
repo_id=repo_id,
folder_path=repo_local_path,
path_in_repo=".",
)

print("Your model is pushed to the Hub. You can view your model here: ", repo_url)

### .

By using push_to_hub you evaluate, record a replay, generate a model card of your agent and push it to the Hub.

This way:

To be able to share your model with the community there are three more steps to follow:

1️⃣ (If it’s not already done) create an account to HF ➡ https://huggingface.co/join

2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.

from huggingface_hub import notebook_login

notebook_login()

If you don’t want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: huggingface-cli login (or login)

3️⃣ We’re now ready to push our trained agent to the 🤗 Hub 🔥 using push_to_hub() function

• Let’s create the model dictionary that contains the hyperparameters and the Q_table.
model = {
"env_id": env_id,
"max_steps": max_steps,
"n_training_episodes": n_training_episodes,
"n_eval_episodes": n_eval_episodes,
"eval_seed": eval_seed,
"learning_rate": learning_rate,
"gamma": gamma,
"max_epsilon": max_epsilon,
"min_epsilon": min_epsilon,
"decay_rate": decay_rate,
"qtable": Qtable_frozenlake,
}

Let’s fill the push_to_hub function:

• repo_id: the name of the Hugging Face Hub Repository that will be created/updated (repo_id = {username}/{repo_name}) 💡 A good repo_id is {username}/q-{env_id}
• model: our model dictionary containing the hyperparameters and the Qtable.
• env: the environment.
• commit_message: message of the commit
model
username = ""  # FILL THIS
repo_name = "q-FrozenLake-v1-4x4-noSlippery"
push_to_hub(repo_id=f"{username}/{repo_name}", model=model, env=env)

Congrats 🥳 you’ve just implemented from scratch, trained and uploaded your first Reinforcement Learning agent. FrozenLake-v1 no_slippery is very simple environment, let’s try an harder one 🔥.

# Part 2: Taxi-v3 🚖

## Create and understand Taxi-v3 🚕

💡 A good habit when you start to use an environment is to check its documentation

In Taxi-v3 🚕, there are four designated locations in the grid world indicated by R(ed), G(reen), Y(ellow), and B(lue).

When the episode starts, the taxi starts off at a random square and the passenger is at a random location. The taxi drives to the passenger’s location, picks up the passenger, drives to the passenger’s destination (another one of the four specified locations), and then drops off the passenger. Once the passenger is dropped off, the episode ends.

env = gym.make("Taxi-v3")

There are 500 discrete states since there are 25 taxi positions, 5 possible locations of the passenger (including the case when the passenger is in the taxi), and 4 destination locations.

state_space = env.observation_space.n
print("There are ", state_space, " possible states")
action_space = env.action_space.n
print("There are ", action_space, " possible actions")

The action space (the set of possible actions the agent can take) is discrete with 6 actions available 🎮:

• 0: move south
• 1: move north
• 2: move east
• 3: move west
• 4: pickup passenger
• 5: drop off passenger

Reward function 💰:

• -1 per step unless other reward is triggered.
• +20 delivering passenger.
• -10 executing “pickup” and “drop-off” actions illegally.
# Create our Q table with state_size rows and action_size columns (500x6)
Qtable_taxi = initialize_q_table(state_space, action_space)
print(Qtable_taxi)
print("Q-table shape: ", Qtable_taxi.shape)

## Define the hyperparameters ⚙️

⚠ DO NOT MODIFY EVAL_SEED: the eval_seed array **allows us to evaluate your agent with the same taxi starting positions for every classmate**
# Training parameters
n_training_episodes = 25000  # Total training episodes
learning_rate = 0.7  # Learning rate

# Evaluation parameters
n_eval_episodes = 100  # Total number of test episodes

# DO NOT MODIFY EVAL_SEED
eval_seed = [
16,
54,
165,
177,
191,
191,
120,
80,
149,
178,
48,
38,
6,
125,
174,
73,
50,
172,
100,
148,
146,
6,
25,
40,
68,
148,
49,
167,
9,
97,
164,
176,
61,
7,
54,
55,
161,
131,
184,
51,
170,
12,
120,
113,
95,
126,
51,
98,
36,
135,
54,
82,
45,
95,
89,
59,
95,
124,
9,
113,
58,
85,
51,
134,
121,
169,
105,
21,
30,
11,
50,
65,
12,
43,
82,
145,
152,
97,
106,
55,
31,
85,
38,
112,
102,
168,
123,
97,
21,
83,
158,
26,
80,
63,
5,
81,
32,
11,
28,
148,
]  # Evaluation seed, this ensures that all classmates agents are trained on the same taxi starting position
# Each seed has a specific starting state

# Environment parameters
env_id = "Taxi-v3"  # Name of the environment
max_steps = 99  # Max steps per episode
gamma = 0.95  # Discounting rate

# Exploration parameters
max_epsilon = 1.0  # Exploration probability at start
min_epsilon = 0.05  # Minimum exploration probability
decay_rate = 0.005  # Exponential decay rate for exploration prob

## Train our Q-Learning agent 🏃

Qtable_taxi = train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_taxi)
Qtable_taxi

## Create a model dictionary 💾 and publish our trained model to the Hub 🔥

- We create a model dictionary that will contain all the training hyperparameters for reproducibility and the Q-Table.
model = {
"env_id": env_id,
"max_steps": max_steps,
"n_training_episodes": n_training_episodes,
"n_eval_episodes": n_eval_episodes,
"eval_seed": eval_seed,
"learning_rate": learning_rate,
"gamma": gamma,
"max_epsilon": max_epsilon,
"min_epsilon": min_epsilon,
"decay_rate": decay_rate,
"qtable": Qtable_taxi,
}
username = ""  # FILL THIS
repo_name = ""
push_to_hub(repo_id=f"{username}/{repo_name}", model=model, env=env)

⚠ To see your entry, you need to go to the bottom of the leaderboard page and click on refresh

# Part 3: Load from Hub 🔽

What’s amazing with Hugging Face Hub 🤗 is that you can easily load powerful models from the community.

1. You go https://huggingface.co/models?other=q-learning to see the list of all the q-learning saved models.
2. You select one and copy its repo_id
1. Then we just need to use load_from_hub with:
• The repo_id
• The filename: the saved model inside the repo.

#### Do not modify this code

from urllib.error import HTTPError

def load_from_hub(repo_id: str, filename: str) -> str:
"""
:param repo_id: id of the model repository from the Hugging Face Hub
:param filename: name of the model zip file from the repository
"""

with open(pickle_model, "rb") as f:

return downloaded_model_file

### .

model = load_from_hub(repo_id="ThomasSimonini/q-Taxi-v3", filename="q-learning.pkl")  # Try to use another model

print(model)
env = gym.make(model["env_id"])

evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
model = load_from_hub(
repo_id="ThomasSimonini/q-FrozenLake-v1-no-slippery", filename="q-learning.pkl"
)  # Try to use another model

env = gym.make(model["env_id"], is_slippery=False)

evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])

The best way to learn **is to try things by your own**! As you saw, the current agent is not doing great. As a first suggestion, you can train for more steps. With 1,000,000 steps, we saw some great results!

In the Leaderboard you will find your agents. Can you get to the top?

Here are some ideas to achieve so:

• Train more steps
• Try different hyperparameters by looking at what your classmates have done.
• Push your new trained model on the Hub 🔥

Are walking on ice and driving taxis too boring to you? Try to change the environment, why not using FrozenLake-v1 slippery version? Check how they work using the gym documentation and have fun 🎉.

Congrats 🥳, you’ve just implemented, trained, and uploaded your first Reinforcement Learning agent.

Understanding Q-Learning is an important step to understanding value-based methods.

In the next Unit with Deep Q-Learning, we’ll see that creating and updating a Q-table was a good strategy — however, this is not scalable.

For instance, imagine you create an agent that learns to play Doom.

Doom is a large environment with a huge state space (millions of different states). Creating and updating a Q-table for that environment would not be efficient.

That’s why we’ll study, in the next unit, Deep Q-Learning, an algorithm where we use a neural network that approximates, given a state, the different Q-values for each action.

See you on Unit 3! 🔥