This repository contains target tracking environments for reinforcement learning (RL) presented in the following papers which applies RL to active target tracking problems.

• Learning to Track Dynamic Targets with Partially Known Environments (https://arxiv.org/abs/2006.10190) : H. Jeong, H. Hassani, M. Morari, D. D. Lee, and G. J. Pappas, “Learning to Track Dynamic Targets with Partially Known Environments,”

• Learning Q-network for Active Information Acquisition (https://arxiv.org/abs/1910.10754, https://ieeexplore.ieee.org/document/8968173) : H. Jeong, B. Schlotfeldt, H. Hassani, M. Morari, D. D. Lee, and G. J. Pappas, “Learning Q-network for Active Information Acquisition,”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macao, China, 2019

It is built with the OpenAI Gym framework and can be run in the same way as you run an OpenAI gym environment.

• Requirements : python3 Install OpenAI gym (http:https://gym.openai.com/docs/)

pip install gym

Clone the repository

git clone https://github.com/coco66/ttenv.git

cd ttenv && source setup

run_example.py shows a brief example of using the ttenv library with a random action selection.

python run_example.py --render 1

See the description in target_tracking.py and target_imtracking.py for more details.

• TargetTracking-v0 : A static target model with Kalman Filter belief tracker. Targets only move with a small noise in the x and y-axis and Kalman Filter is used for the belief estimate. A target state of each target consists of its x,y position.

• TargetTracking-v1 : A double integrator target model with Kalman Filter belief tracker. A dynamic model of each target follows a double integrator and Kalman Filter is used for the belief estimate. A target state of each target consists of its x,y position and xdot, ydot.

• TargetTracking-v2 : SE2 Target model with UKF belief tracker A dynamic model of each target follows the SE2 dynamics with a certain control policy for linear and angular velocities. The target state consists of x, y, and yaw.

• TargetTracking-v3 : SE2 Target model with UKF belief tracker (including velocity estimates). Similar to the v3 environment but the UKF estimates not only x, y, and yaw but also the control policy (linear and angular velocities) of each target.

• TargetTracking-v4 : A double integrator target model with Kalman Filter belief tracker. A map state included.

• TargetTracking-v5 : A double integrator target model with Kalman Filter belief tracker. A map and visit-frequency states are included.

Initial positions of targets and the agent significantly influence the reward.
Currently, you can randomly initialize them within a certain conditions or provide a set of initial position as a pickle file.

• Random initialization
  The agent's xy position and yaw are always randomly generalized in a given map. If the map is empty, the agent's initial position is the center of the map and only its yaw is randomly initialized. Values for the following variables can be provided (or use the default values) for the initialization of targets and target beliefs.
  • A range for the linear distance from the agent to a target or a belief target.
  • A range for the angular distance from the agent to a target.
  • A range for the angular distance from the agent to a belief target.
  • A boolean variable whether there is an obstacle between the agent and a target.
• Initialization with a given a list of poses You can generate a list of initial positions for the agent, targets, and belief targets using ttenv/gen_init_pose.py.

Some metadata examples for the environment setup are presented in ttenv/ttenv/metatdata.py. METADATA_v0 was used for the experiments presented in “Learning Q-network for Active Information Acquisition”. It is slightly different due to the update on the base structure of the repository. METADATA_v1 and METADATA_multi_v1 were used for the experiments presented in “Learning to Track Dynamic Targets with Partially Known Environments”.

Examples of learning a deep reinforcement learning policy can be found in the ADFQ repository (https://github.com/coco66/ADFQ).

• DQN : ADFQ/deep_adfq/baselines0/deepq/run_tracking.py
• Double DQN : ADFQ/deep_adfq/baselines0/deepq/run_tracking.py --double_q=1
• Deep ADFQ : ADFQ/deep_adfq/run_tracking.py

A search-based planning method used as a baseline in the above paper, Learning Q-network for Active Information Acquisition, can be used with this library. To use, download and install the InfoPlanner (https://bitbucket.org/brentsc/infoplanner.git) following its installation instruction. Note that it works only with python version 3.5.

export PYTHONPATH="${PYTHONPATH}:YOUR-PATH-TO-INFOPLANNER/lib"
python ttenv/infoplanner_python/run_anytime_planner.py --render 1

If you use this repo in your research, you can cite it as follows:

@misc{ttenv,
    author = {Heejin Jeong, Brent Schlotfeldt, Hamed Hassani, Manfred Morari, Daniel D. Lee, and George J. Pappas},
    title = {Target tracking environments for Reinforcement Learning},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/coco66/ttenv.git}},
}