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Rapidly exploring random trees with machine learning

Project description


Rapidly exploring random trees with machine learning - learned sampling distributions, local reinforcement learning controller and learned supervised distance function for car-like mobile robots

Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. License
  5. Acknowledgments

About The Project

This project unites optimal rapidly exploring random trees (RRT*) with the following machine learning techniques:

The experiments are conducted in PyBullet with a car-like mobile robot in a narrow passage type of scenario. The code allows the training and testing of each machine learning modules individually, but also in the context of the broader RRT* approach.

Getting Started

Follow the instructions below to install the package.


You need to install PyTorch and PyBullet.


The easiest way is to use conda.

If you have a CUDA-enabled GPU:

conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia

If you want to use CPU only:

conda install pytorch torchvision torchaudio cpuonly -c pytorch


Installing PyBullet with pip requires build tools. I recommend using conda:

conda install -c conda-forge pybullet


Install the package with pip:

pip install rrt-ml

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You can run experiments with different parameters for each module. Run from the command line or from any python file.


Run experiments from the command line:

rrt-ml (--rl | --sl | --rrt) (--train | --test) [--hyper] [--config CONFIG]
  1. The first option controls which algorithm will run:
  • --rl: reinforcement learning agent as a local controller
  • --sl: "sample learner" to learn sampling distributions for optimal motion planning with RRT*
  • --rrt: optimal rapidly-exploring random tree
  1. The second option controls how should it run:
  • --train: train a machine learning model (RL or SL) or grow the RRT* tree
  • --test: generate various results (only after training)
  1. The third option, --hyper, determines whether a search for hyperparameters should be made. If the second option is --train, then each possible config of the grid search is trained. If the second option is --test, then a bunch of results will be produced in order to compare different models (only after training).

  2. The fourth option specify the name of the config. Below there are instructions to create a config file or directly run it, without using the command line.

Configuring an Experiment

Create a python file anywhere. Create a MasterConfig object and set a name to it:

from rrt_ml.utilities.configs import MasterConfig

cfg = MasterConfig()

cfg.general.config_name_or_prefix = "MyExperimentConfigName"

Your IDE should auto-complete cfg and show all nested attributes. You can change various settings for all algorithms individually:

# Change reinforcement learning agent config
cfg.rl.general.gamma = 0.98 = 0.01 = 0.01

# Change sample learner (beta-cvae) config = 5 = 128

# Change RRT* config
cfg.rrt.general.seed = 1
cfg.rrt.sample.goal_prob = 0.05
cfg.rrt.names.rl = 'best'  # Use config name 'best' as controller for RRT = 'best'  # Use config name 'best' as sample generator

You can also set a grid search over any setting that exists inside the rl|sl|rrt attributes above. Below an example on how to setup a grid search over the hyper-parameters for the RL agent:

cfg.hyperparams.rl.general.gamma = [0.95, 0.96, 0.97, 0.98, 0.99] = [0.1, 0.01, 0.001] = ['ReLU', 'GeLU']

Now you can save the config to run later from the command line:

Training an RL agent with this example config from the command line:

rrt-ml --rl --train --config=MyExperimentConfigName

Instead of saving and the running from the command line, you can simply run it from the python file you are editing, by adding this:'rl', train_or_test='train', hyperparam_search_or_test=False)

The parameters of the run method are equal to the ones from the command line.

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Distributed under the MIT License. See LICENSE.txt for more information.

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