This contains the radiation source search environment proposed by Proctor et al. and their published paper.

Below is a demo of a test episode where the trained deep reinforcement learning agent is controlling a radiation detector to search for a gamma radiation source in a non-convex environment (7 obstructions).

The obstructions (gray rectangles) block line of sight between the detector and gamma source resulting in the detector only measuring background radiation. The left plot shows the detector positions (black triangles) in the environment, the agent's source location prediction (magenta circles), and the gamma source (red star). The middle plot shows the measured gamma radiation intensity at each timestep and the right plot show the cumulative reward that the agent receives from its selected actions during an episode that is used during training to update the neural network weights. The episode terminates if the detector comes within 1.1 m of the gamma source (success) or if the episode length reaches the episode max.

This contains the RAD-A2C architecture and proximal policy optimization (PPO) for radiation source search from our Base code from OpenAI's Spinningup repo.

This repository provides a Minimal PyTorch implementation of Proximal Policy Optimization (PPO) with clipped objective from Nikhil Barhate's [PPO-PyTorch] (https://github.com/nikhilbarhate99/PPO-PyTorch) repo.

It is recommended to use the Anaconda package manager.

1. After cloning this repository, create a virtual environment with the required packages conda env create -f <PATH-TO-ALGORTIHM>/environment.yml. Then activate this environment with conda activate <ENV_NAME>. The radiation_ppo code requires OpenMPI for parallel processing.

In VSCode, you can run the specific algorithm and environment configuration through the Command Palette (Ctrl+Shift+P) by filtering on Debug: Select and Start Debugging or typing 'debug ' and selecting the configuration you want to run.

• /algo: contains the PPO implementations and neural network architectures
• /gym_rad_search: contains the radiation source search OpenAI gym environment