rnl 0.3.4

RNL - Robot Navigation Learning

Robot Navigation Learning

End-to-end Deep Reinforcement Learning for Real-World Robotics Navigation in Pytorch

This project uses Deep Reinforcement Learning (DRL) to train a robot to move in unfamiliar environments. The robot learns to make decisions on its own, interacting with the environment, and gradually becomes better and more efficient at navigation.

How to Use

Installation and usage mode.

• Install with pip:

pip install rnl

• Use train:

import numpy as np
import rnl as vault

# 1.step -> config robot
param_robot = vault.robot(
    base_radius=0.033,  # (m)
    vel_linear=[0.0, 2.0],  # [min, max]
    vel_angular=[1.0, 2.0],  # [min, max]
    wheel_distance=0.16,  # (cm)
    weight=1.0,  # robot (kg)
    threshold=0.01,  # distance for obstacle avoidance (cm)
)

# 2.step -> config sensors [for now only lidar sensor!!]
param_sensor = vault.sensor(
    fov=2 * np.pi,
    num_rays=20,
    min_range=0.0,
    max_range=6.0,
)

# 3.step -> config env
param_env = vault.make(
    map_file="None", # map file yaml (Coming soon)
    random_mode="normal",  # hard or normal (Coming soon)
    timestep=1000,  # max timestep
    grid_dimension=5,  # size grid
    friction=0.4,  # grid friction
    porcentage_obstacles=0.1
)

# 4.step -> config train robot
model = vault.Trainer(
    param_robot, param_sensor, param_env, pretrained_model=False
)

# 5.step -> train robot
model.learn(
    batch_size=64,
    lr=0.0001,
    seed=1,
    num_envs=2,
    device="cpu",
    target_score=200,
    checkpoint=100,
    checkpoint_path="checkpoints",
    hidden_size=[800, 600],
)

• Use inference:

import numpy as np
import rnl as vault

# 1.step -> config robot
param_robot = vault.robot(
    base_radius=0.033,  # (m)
    vel_linear=[0.0, 2.0],  # [min, max]
    vel_angular=[1.0, 2.0],  # [min, max]
    wheel_distance=0.16,  # (cm)
    weight=1.0,  # robot (kg)
    threshold=0.01,  # distance for obstacle avoidance (cm)
)

# 2.step -> config sensors [for now only lidar sensor!!]
param_sensor = vault.sensor(
    fov=2 * np.pi,
    num_rays=20,
    min_range=0.0,
    max_range=6.0,
)

# 3.step -> config env
param_env = vault.make(
    map_file="None", # map file yaml (Coming soon)
    random_mode="normal",  # hard or normal (Coming soon)
    timestep=1000,  # max timestep
    grid_dimension=5,  # size grid
    friction=0.4,  # grid friction
    porcentage_obstacles=0.1
)

# 4.step -> config render
param_render = vault.render(fps=100, controller=True, rgb_array=True)


# 5.step -> config train robot
model = vault.Trainer(
    param_robot, param_sensor, param_env, param_render, pretrained_model=False
)

# 6.step -> run robot
model.run()

• Use demo:

python train.py

License

This project is licensed under the MIT license - see archive LICENSE for details.

Contact and Contribution

The project is still under development and may have some bugs. If you encounter any problems or have suggestions, feel free to open an issue or send an email to: Nicolas Alan - [e-mail].

TODO:

• Add map file yaml
• Add random mode (hard or normal)
• Create Integration ROS and (Gazebo)
• Create Integration with OpenAI

Acknowledgments

• AgileRL
• Gazebo