A framework for collision probability distribution estimation via temporal difference learning.
In today's deep learning era, understanding the inner workings of complex models is of major interest. Enter CollisionPro, a noval framework designed to estimate cumulative collision probability distributions through temporal difference learning.
But what sets CollisionPro apart? It's all about transparency and explainability. In an age where black-box algorithms reign supreme, CollisionPro takes a step towards explainable AI. By generating a cumulative probability distribution, it offers not just a single risk assessment value, but a spectrum of interpretable values. Moreover, each value could be interpreted in the context of reinforcement learning.
To effortlessly install CollisionPro, utilize the following pip command:
pip3 install git+https://github.com/UniBwTAS/CollisionPro.git#egg=CollisionProor
python3 -m pip install git+https://github.com/UniBwTAS/CollisionPro.git#egg=CollisionProAfter a successful installation, ensure the correctness of the setup by following these steps:
- Open a new terminal window
- Launch the Python 3 shell:
python3- Verify the installation by importing the CollisionPro module:
import collisionproWe provide some examples for an easier/faster understanding of CollisionPro. Each example consists of an environment (env.py), an approximator (approximator.py), that learns the probability distribution, a controller (controller.py) if required, that provides an action given the current state, and a main script (main.py) that is setting up the training pipeline. For more information see HowTo Guide.
Once you have installed CollisionPro (see Installation), you can easily run our examples:
Random Walk (Easy Example)
The Random Walk environment consists of sequential states, where the first and last states indicate terminal states. The agent transitions with a 0.5 probability to the right and a 0.5 probability to the left. For each non-terminal state, the agent receives a reward of r=0, while for terminal states, the agent receives a reward of r=-1, which corresponds to a collision.
- No action space → no controller
- No value function approximation
from collisionpro.examples.random_walk.main import run
run()Moving Circles (Advanced Example)
The Moving Circles environment is centered around the ego circle (blue), which moves along the x-axis with a constant linear velocity. Obstacles (red circles) oscillate along the y-axis and can be modeled as mass-spring systems (no energy dissipation). When the outer circles of the ego and any obstacle overlap, a collision event is triggered, terminating the episode and resulting in a reward of -1.
Both the ego and obstacles predict each other's movements using constant kinematics into the future and adjust their positions accordingly. The ego ball can move along the y-axis and is modeled in y-direction as a mass-spring-damper system, while the obstacles can move along the x-axis.
- Action space available → controller provided
- Value function approximation via TensorFlow
- Multi-agent, partially observable, continuous environment
Execute the following, which will iteratively generate samples, learn the collision probability distribution and finally generates collision characteristics that are plotted.
from collisionpro.examples.moving_circles.main import run
run()Find a comprehensive Guide for CollisionPro functionalities and examples here.