Now that you’ve studied the theory behind Advantage Actor Critic (A2C), you’re ready to train your A2C agent using Stable-Baselines3 in a robotic environment. And train a:
We’re going to use
To validate this hands-on for the certification process, you need to push your two trained models to the Hub and get the following results:
PandaReachDense-v3
get a result of >= -3.5.To find your result, go to the 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
To start the hands-on click on Open In Colab button 👇 :
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.
At the end of the notebook, you will:
Before diving into the notebook, you need to:
🔲 📚 Study Actor-Critic methods by reading Unit 6 🤗
Runtime > Change Runtime type
Hardware Accelerator > GPU
During the notebook, we’ll need to generate a replay video. To do so, with colab, we need to have a virtual screen to be able to render the environment (and thus record the frames).
The following cell will install the librairies and create and run a virtual screen 🖥
%%capture !apt install python-opengl !apt install ffmpeg !apt install xvfb !pip3 install pyvirtualdisplay
# Virtual display
from pyvirtualdisplay import Display
virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()
We’ll install multiple ones:
gymnasium
panda-gym
: Contains the robotics arm environments.stable-baselines3
: The SB3 deep reinforcement learning library.huggingface_sb3
: Additional code for Stable-baselines3 to load and upload models from the Hugging Face 🤗 Hub.huggingface_hub
: Library allowing anyone to work with the Hub repositories.!pip install stable-baselines3[extra] !pip install gymnasium !pip install huggingface_sb3 !pip install huggingface_hub !pip install panda_gym
import os
import gymnasium as gym
import panda_gym
from huggingface_sb3 import load_from_hub, package_to_hub
from stable_baselines3 import A2C
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3.common.env_util import make_vec_env
from huggingface_hub import notebook_login
The agent we’re going to train is a robotic arm that needs to do controls (moving the arm and using the end-effector).
In robotics, the end-effector is the device at the end of a robotic arm designed to interact with the environment.
In PandaReach
, the robot must place its end-effector at a target position (green ball).
We’re going to use the dense version of this environment. It means we’ll get a dense reward function that will provide a reward at each timestep (the closer the agent is to completing the task, the higher the reward). Contrary to a sparse reward function where the environment return a reward if and only if the task is completed.
Also, we’re going to use the End-effector displacement control, it means the action corresponds to the displacement of the end-effector. We don’t control the individual motion of each joint (joint control).
This way the training will be easier.
In PandaReachDense-v3
the robotic arm must place its end-effector at a target position (green ball).
env_id = "PandaReachDense-v3"
# Create the env
env = gym.make(env_id)
# Get the state space and action space
s_size = env.observation_space.shape
a_size = env.action_space
print("_____OBSERVATION SPACE_____ \n")
print("The State Space is: ", s_size)
print("Sample observation", env.observation_space.sample()) # Get a random observation
The observation space is a dictionary with 3 different elements:
achieved_goal
: (x,y,z) position of the goal.desired_goal
: (x,y,z) distance between the goal position and the current object position.observation
: position (x,y,z) and velocity of the end-effector (vx, vy, vz).Given it’s a dictionary as observation, we will need to use a MultiInputPolicy policy instead of MlpPolicy.
print("\n _____ACTION SPACE_____ \n")
print("The Action Space is: ", a_size)
print("Action Space Sample", env.action_space.sample()) # Take a random action
The action space is a vector with 3 values:
A good practice in reinforcement learning is to normalize input features.
For that purpose, there is a wrapper that will compute a running average and standard deviation of input features.
We also normalize rewards with this same wrapper by adding norm_reward = True
You should check the documentation to fill this cell
env = make_vec_env(env_id, n_envs=4)
# Adding this wrapper to normalize the observation and the reward
env = # TODO: Add the wrapper
env = make_vec_env(env_id, n_envs=4)
env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10.)
For more information about A2C implementation with StableBaselines3 check: https://stable-baselines3.readthedocs.io/en/master/modules/a2c.html#notes
To find the best parameters I checked the official trained agents by Stable-Baselines3 team.
model = # Create the A2C model and try to find the best parameters
model = A2C(policy = "MultiInputPolicy",
env = env,
verbose=1)
model.learn(1_000_000)
# Save the model and VecNormalize statistics when saving the agent
model.save("a2c-PandaReachDense-v3")
env.save("vec_normalize.pkl")
evaluate_policy
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
# Load the saved statistics
eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v3")])
eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)
# We need to override the render_mode
eval_env.render_mode = "rgb_array"
# do not update them at test time
eval_env.training = False
# reward normalization is not needed at test time
eval_env.norm_reward = False
# Load the agent
model = A2C.load("a2c-PandaReachDense-v3")
mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
Now that we saw we got good results after the training, we can publish our trained model on the Hub with one line of code.
📚 The libraries documentation 👉 https://github.com/huggingface/huggingface_sb3/tree/main#hugging-face—x-stable-baselines3-v20
By using package_to_hub
, as we already mentionned in the former units, 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.
notebook_login()
!git config --global credential.helper store
If you don’t want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: huggingface-cli login
3️⃣ We’re now ready to push our trained agent to the 🤗 Hub 🔥 using package_to_hub()
function.
For this environment, running this cell can take approximately 10min
from huggingface_sb3 import package_to_hub
package_to_hub(
model=model,
model_name=f"a2c-{env_id}",
model_architecture="A2C",
env_id=env_id,
eval_env=eval_env,
repo_id=f"ThomasSimonini/a2c-{env_id}", # Change the username
commit_message="Initial commit",
)
The best way to learn is to try things by your own! Why not trying PandaPickAndPlace-v3
?
If you want to try more advanced tasks for panda-gym, you need to check what was done using TQC or SAC (a more sample-efficient algorithm suited for robotics tasks). In real robotics, you’ll use a more sample-efficient algorithm for a simple reason: contrary to a simulation if you move your robotic arm too much, you have a risk of breaking it.
PandaPickAndPlace-v1 (this model uses the v1 version of the environment): https://huggingface.co/sb3/tqc-PandaPickAndPlace-v1
And don’t hesitate to check panda-gym documentation here: https://panda-gym.readthedocs.io/en/latest/usage/train_with_sb3.html
We provide you the steps to train another agent (optional):
package_to_hub
# 1 - 2
env_id = "PandaPickAndPlace-v3"
env = make_vec_env(env_id, n_envs=4)
# 3
env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10.)
# 4
model = A2C(policy = "MultiInputPolicy",
env = env,
verbose=1)
# 5
model.learn(1_000_000)
# 6
model_name = "a2c-PandaPickAndPlace-v3";
model.save(model_name)
env.save("vec_normalize.pkl")
# 7
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
# Load the saved statistics
eval_env = DummyVecEnv([lambda: gym.make("PandaPickAndPlace-v3")])
eval_env = VecNormalize.load("vec_normalize.pkl", eval_env)
# do not update them at test time
eval_env.training = False
# reward normalization is not needed at test time
eval_env.norm_reward = False
# Load the agent
model = A2C.load(model_name)
mean_reward, std_reward = evaluate_policy(model, eval_env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
# 8
package_to_hub(
model=model,
model_name=f"a2c-{env_id}",
model_architecture="A2C",
env_id=env_id,
eval_env=eval_env,
repo_id=f"ThomasSimonini/a2c-{env_id}", # TODO: Change the username
commit_message="Initial commit",
)
See you on Unit 7! 🔥