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@@ -1,42 +1,10 @@
# Hands-on [[hands-on]]
<CourseFloatingBanner classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit2/unit2.ipynb"}
]}
askForHelpUrl="http://hf.co/join/discord" />
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)](https://www.gymlibrary.dev/environments/toy_text/frozen_lake/) ☃️ : 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](https://www.gymlibrary.dev/environments/toy_text/taxi/) 🚖 will need **to learn to navigate** a city to **transport its passengers from point A to point B.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/envs.gif" alt="Environments"/>
Thanks to a [leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-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](https://huggingface.co/deep-rl-course/en/unit0/introduction#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](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward**
For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
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 the Open In Colab button** 👇 :
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit2/unit2.ipynb)
<a href="https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/notebooks/unit2/unit2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Unit 2: Q-Learning with FrozenLake-v1 ⛄ and Taxi-v3 🚕
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/thumbnail.jpg" alt="Unit 2 Thumbnail">
In this notebook, **you'll code your first Reinforcement Learning agent from scratch** to play FrozenLake ❄️ using Q-Learning, share it with the community, and experiment with different configurations.
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.** ⬇️
@@ -44,12 +12,12 @@ In this notebook, **you'll code your first Reinforcement Learning agent from scr
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/envs.gif" alt="Environments"/>
### 🎮 Environments:
###🎮 Environments:
- [FrozenLake-v1](https://www.gymlibrary.dev/environments/toy_text/frozen_lake/)
- [Taxi-v3](https://www.gymlibrary.dev/environments/toy_text/taxi/)
### 📚 RL-Library:
###📚 RL-Library:
- Python and NumPy
- [Gym](https://www.gymlibrary.dev/)
@@ -61,34 +29,52 @@ We're constantly trying to improve our tutorials, so **if you find some issues i
At the end of the notebook, you will:
- Be able to use **Gym**, the environment library.
- Be able to code a Q-Learning agent from scratch.
- 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 🔥.
## This notebook is from the Deep Reinforcement Learning Course
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/deep-rl-course-illustration.jpg" alt="Deep RL Course illustration"/>
In this free course, you will:
- 📖 Study Deep Reinforcement Learning in **theory and practice**.
- 🧑‍💻 Learn to **use famous Deep RL libraries** such as Stable Baselines3, RL Baselines3 Zoo, CleanRL and Sample Factory 2.0.
- 🤖 Train **agents in unique environments**
And more check 📚 the syllabus 👉 https://simoninithomas.github.io/deep-rl-course
Don’t forget to **<a href="http://eepurl.com/ic5ZUD">sign up to the course</a>** (we are collecting your email to be able to **send you the links when each Unit is published and give you information about the challenges and updates).**
The best way to keep in touch is to join our discord server to exchange with the community and with us 👉🏻 https://discord.gg/ydHrjt3WP5
## Prerequisites 🏗️
Before diving into the notebook, you need to:
🔲 📚 **Study [Q-Learning by reading Unit 2](https://huggingface.co/deep-rl-course/unit2/introduction)** 🤗
🔲 📚 **Study [Q-Learning by reading Unit 2](https://huggingface.co/deep-rl-course/unit2/introduction)** 🤗
## A small recap of Q-Learning
- *Q-Learning* **is the RL algorithm that**
- Trains *Q-Function*, an **action-value function** that encoded, in internal memory, by a *Q-table* **that contains all the state-action pair values.**
- Given a state and action, our Q-Function **will search the Q-table for the corresponding value.**
- 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.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-function-2.jpg" alt="Q function" width="100%"/>
- 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, the best action to take.**
have an optimal policy,since we **know for each state, what is the best action to take.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/link-value-policy.jpg" alt="Link value policy" width="100%"/>
But, in the beginning, our **Q-Table is useless since it gives arbitrary values for each state-action pair (most of the time we initialize the Q-Table to 0 values)**. But, as we explore the environment and update our Q-Table it will give us better and better approximations
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
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/q-learning.jpeg" alt="q-learning.jpeg" width="100%"/>
@@ -99,6 +85,13 @@ This is the Q-Learning pseudocode:
# Let's code our first Reinforcement Learning algorithm 🚀
To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#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](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) and find your model, **the result = mean_reward - std of reward**
For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
## 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).
@@ -113,19 +106,20 @@ We’ll install multiple ones:
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 all the Deep RL models available here (if they use Q Learning) 👉 https://huggingface.co/models?other=q-learning
You can see here all the Deep RL models available (if they use Q Learning) 👉 https://huggingface.co/models?other=q-learning
```bash
pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit2/requirements-unit2.txt
```python
!pip install -r https://raw.githubusercontent.com/huggingface/deep-rl-class/main/notebooks/unit2/requirements-unit2.txt
```
```bash
sudo apt-get update
apt install python-opengl ffmpeg xvfb
pip3 install pyvirtualdisplay
```python
%%capture
!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 to this trick, **we will be able to run our virtual screen.**
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.**
```python
import os
@@ -154,6 +148,7 @@ import gym
import random
import imageio
import os
import tqdm
import pickle5 as pickle
from tqdm.notebook import tqdm
@@ -163,10 +158,10 @@ We're now ready to code our Q-Learning algorithm 🔥
# Part 1: Frozen Lake ⛄ (non slippery version)
## Create and understand [FrozenLake environment ⛄](https://www.gymlibrary.dev/environments/toy_text/frozen_lake/)
## Create and understand [FrozenLake environment ⛄]((https://www.gymlibrary.dev/environments/toy_text/frozen_lake/)
---
💡 A good habit when you start to use an environment is to check its documentation
💡 A good habit when you start to use an environment is to check its documentation
👉 https://www.gymlibrary.dev/environments/toy_text/frozen_lake/
@@ -217,7 +212,7 @@ 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)**.
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.**
@@ -253,18 +248,17 @@ It's time to initialize our Q-table! To know how many rows (states) and columns
```python
state_space =
state_space =
print("There are ", state_space, " possible states")
action_space =
action_space =
print("There are ", action_space, " possible actions")
```
```python
# 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 =
Qtable =
return Qtable
```
@@ -299,7 +293,7 @@ Remember we have two policies since Q-Learning is an **off-policy** algorithm. T
- Epsilon-greedy policy (acting policy)
- Greedy-policy (updating policy)
The greedy policy will also be the final policy we'll have when the Q-learning agent completes training. The greedy policy is used to select an action using the Q-table.
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.
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/off-on-4.jpg" alt="Q-Learning" width="100%"/>
@@ -307,8 +301,8 @@ The greedy policy will also be the final policy we'll have when the Q-learning a
```python
def greedy_policy(Qtable, state):
# Exploitation: take the action with the highest state, action value
action =
action =
return action
```
@@ -330,9 +324,9 @@ 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 a random action).
- With *probability ɛ*: we do **exploration** (trying random action).
As the training continues, we progressively **reduce the epsilon value since we will need less and less exploration and more exploitation.**
And as the training goes, we progressively **reduce the epsilon value since we will need less and less exploration and more exploitation.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-learning-4.jpg" alt="Q-Learning" width="100%"/>
@@ -340,16 +334,16 @@ As the training continues, we progressively **reduce the epsilon value since we
```python
def epsilon_greedy_policy(Qtable, state, epsilon):
# Randomly generate a number between 0 and 1
random_num =
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 =
action =
# else --> exploration
else:
action = # Take a random action
return action
```
@@ -372,7 +366,7 @@ def epsilon_greedy_policy(Qtable, state, epsilon):
```
## Define the hyperparameters ⚙️
The exploration related hyperparameters are some of the most important ones.
The exploration related hyperparamters 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.
@@ -409,7 +403,7 @@ 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:
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)]
@@ -419,7 +413,7 @@ Reset the environment
```python
def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable):
for episode in range(n_training_episodes):
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
@@ -430,19 +424,19 @@ def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_st
# repeat
for step in range(max_steps):
# Choose the action At using epsilon greedy policy
action =
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 =
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] =
Qtable[state][action] =
# If done, finish the episode
if done:
break
# Our next state is the new state
state = new_state
return Qtable
@@ -674,19 +668,19 @@ def push_to_hub(repo_id, model, env, video_fps=1, local_repo_path="hub"):
metadata = {**metadata, **eval}
model_card = f"""
# **Q-Learning** Agent playing1 **{env_id}**
This is a trained model of a **Q-Learning** agent playing **{env_id}** .
# **Q-Learning** Agent playing1 **{env_id}**
This is a trained model of a **Q-Learning** agent playing **{env_id}** .
## Usage
## Usage
```python
```python
model = load_from_hub(repo_id="{repo_id}", filename="q-learning.pkl")
model = load_from_hub(repo_id="{repo_id}", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
"""
# 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"])
@@ -726,7 +720,7 @@ By using `push_to_hub` **you evaluate, record a replay, generate a model card of
This way:
- You can **showcase our work** 🔥
- You can **visualize your agent playing** 👀
- You can **share an agent with the community that others can use** 💾
- You can **share with the community an agent that others can use** 💾
- You can **access a leaderboard 🏆 to see how well your agent is performing compared to your classmates** 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
@@ -788,21 +782,21 @@ 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 a harder one 🔥.
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 🚕](https://www.gymlibrary.dev/environments/toy_text/taxi/)
---
💡 A good habit when you start to use an environment is to check its documentation
💡 A good habit when you start to use an environment is to check its documentation
👉 https://www.gymlibrary.dev/environments/toy_text/taxi/
---
In `Taxi-v3` 🚕, there are four designated locations in the grid world indicated by R(ed), G(reen), Y(ellow), and B(lue).
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.
@@ -1009,7 +1003,7 @@ repo_name = ""
push_to_hub(repo_id=f"{username}/{repo_name}", model=model, env=env)
```
Now that it's on the Hub, you can compare the results of your Taxi-v3 with your classmates using the leaderboard 🏆 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
Now that's on the Hub, you can compare the results of your Taxi-v3 with your classmates using the leaderboard 🏆 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
⚠ To see your entry, you need to go to the bottom of the leaderboard page and **click on refresh** ⚠
@@ -1075,36 +1069,36 @@ evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"
```
## Some additional challenges 🏆
The best way to learn **is to try things on 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!
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](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?
Here are some ideas to climb up the leaderboard:
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 use the FrozenLake-v1 slippery version? Check how they work [using the gym documentation](https://www.gymlibrary.dev/) and have fun 🎉.
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](https://www.gymlibrary.dev/) 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 while creating and updating a Q-table was a good strategy — **however, it is not scalable.**
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.
For instance, imagine you create an agent that learns to play Doom.
<img src="https://vizdoom.cs.put.edu.pl/user/pages/01.tutorial/basic.png" alt="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.
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 Deep Q-Learning in the next unit, an algorithm **where we use a neural network that approximates, given a state, the different Q-values for each action.**
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.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/atari-envs.gif" alt="Environments"/>
See you in Unit 3! 🔥
See you on Unit 3! 🔥
## Keep learning, stay awesome 🤗
## Keep learning, stay awesome 🤗