Adding Air Learning to Project list (#471)

* Adding Air Learning to Project list

* Minor: reformat

docs/misc/projects.rst

@@ -9,7 +9,6 @@ Please tell us, if you want your project to appear on this page ;)
 
 Learning to drive in a day
 --------------------------
-
 Implementation of reinforcement learning approach to make a donkey car learn to drive.
 Uses DDPG on VAE features (reproducing paper from wayve.ai)
 
@@ -19,7 +18,6 @@ Uses DDPG on VAE features (reproducing paper from wayve.ai)
 
 Donkey Gym
 ----------
-
 OpenAI gym environment for donkeycar simulator.
 
 | Author: Tawn Kramer (@tawnkramer)
@@ -28,7 +26,6 @@ OpenAI gym environment for donkeycar simulator.
 
 Self-driving FZERO Artificial Intelligence
 ------------------------------------------
-
 Series of videos on how to make a self-driving FZERO artificial intelligence using reinforcement learning algorithms PPO2 and A2C.
 
 | Author: Lucas Thompson
@@ -37,7 +34,6 @@ Series of videos on how to make a self-driving FZERO artificial intelligence usi
 
 S-RL Toolbox
 ------------
-
 S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics.
 Stable-Baselines was originally developped for this project.
 
@@ -47,28 +43,22 @@ Stable-Baselines was originally developped for this project.
 
 Roboschool simulations training on Amazon SageMaker
 ---------------------------------------------------
-
 "In this notebook example, we will make HalfCheetah learn to walk using the stable-baselines [...]"
 
-
 | Author: Amazon AWS
 | `Repo Link <https://github.com/awslabs/amazon-sagemaker-examples/tree/master/reinforcement_learning/rl_roboschool_stable_baselines>`_
 
 
 MarathonEnvs + OpenAi.Baselines
 -------------------------------
-
-
 Experimental - using OpenAI baselines with MarathonEnvs (ML-Agents)
 
-
 | Author: Joe Booth (@Sohojoe)
 | Github repo: https://github.com/Sohojoe/MarathonEnvsBaselines
 
 
 Learning to drive smoothly in minutes
 -------------------------------------
-
 Implementation of reinforcement learning approach to make a car learn to drive smoothly in minutes.
 Uses SAC on VAE features.
 
@@ -79,7 +69,6 @@ Uses SAC on VAE features.
 
 Making Roboy move with elegance
 -------------------------------
-
 Project around Roboy, a tendon-driven robot, that enabled it to move its shoulder in simulation to reach a pre-defined point in 3D space. The agent used Proximal Policy Optimization (PPO) or Soft Actor-Critic (SAC) and was tested on the real hardware.
 
 | Authors: Alexander Pakakis, Baris Yazici, Tomas Ruiz
@@ -91,9 +80,9 @@ Project around Roboy, a tendon-driven robot, that enabled it to move its shoulde
 | Blog post: https://tinyurl.com/mediumDRC
 | Website: https://roboy.org/
 
+
 Train a ROS-integrated mobile robot (differential drive) to avoid dynamic objects
 ---------------------------------------------------------------------------------
-
 The RL-agent serves as local planner and is trained in a simulator, fusion of the Flatland Simulator and the crowd simulator Pedsim. This was tested on a real mobile robot.
 The Proximal Policy Optimization (PPO) algorithm is applied.
 
@@ -102,9 +91,9 @@ The Proximal Policy Optimization (PPO) algorithm is applied.
 | Video: https://www.youtube.com/watch?v=laGrLaMaeT4
 | GitHub: https://github.com/RGring/drl_local_planner_ros_stable_baselines
 
+
 Adversarial Policies: Attacking Deep Reinforcement Learning
 -----------------------------------------------------------
-
 Uses Stable Baselines to train *adversarial policies* that attack pre-trained victim policies in a zero-sum multi-agent environments.
 May be useful as an example of how to integrate Stable Baselines with `Ray <https://github.com/ray-project/ray>`_ to perform distributed experiments and `Sacred <https://github.com/IDSIA/sacred>`_ for experiment configuration and monitoring.
 
@@ -114,6 +103,7 @@ May be useful as an example of how to integrate Stable Baselines with `Ray <http
 | Paper: https://arxiv.org/abs/1905.10615
 | Website: https://adversarialpolicies.github.io
 
+
 WaveRL: Training RL agents to perform active damping
 ----------------------------------------------------
 Reinforcement learning is used to train agents to control pistons attached to a bridge to cancel out vibrations.  The bridge is modeled as a one dimensional oscillating system and dynamics are simulated using a finite difference solver.  Agents were trained using Proximal Policy Optimization.  See presentation for environment detalis.
@@ -123,16 +113,26 @@ Reinforcement learning is used to train agents to control pistons attached to a
 | GitHub: https://github.com/jaberkow/WaveRL
 | Presentation: http://bit.ly/WaveRLslides
 
+
 Fenics-DRL: Fluid mechanics and Deep Reinforcement Learning
 -----------------------------------------------------------
-
 Deep Reinforcement Learning is used to control the position or the shape of obstacles in different fluids in order to optimize drag or lift. `Fenics <https://fenicsproject.org>`_ is used for the Fluid Mechanics part, and Stable Baselines is used for the DRL.
 
-
 | Authors: Paul Garnier, Jonathan Viquerat, Aurélien Larcher, Elie Hachem
 | Email: paul.garnier@mines-paristech.fr
 | GitHub: https://github.com/DonsetPG/openFluid
 | Paper: https://arxiv.org/abs/1908.04127
 | Website: https://donsetpg.github.io/blog/2019/08/06/DRL-FM-review/
 
 
+Air Learning: An AI Research Platform Algorithm Hardware Benchmarking of Autonomous Aerial Robots
+-------------------------------------------------------------------------------------------------
+Aerial robotics is a cross-layer, interdisciplinary field. Air Learning is an effort to bridge seemingly disparate fields.
+
+Designing an autonomous robot to perform a task involves interactions between various boundaries spanning from modeling the environment down to the choice of onboard computer platform available in the robot. Our goal through building Air Learning is to provide researchers with a cross-domain infrastructure that allows them to holistically study and evaluate reinforcement learning algorithms for autonomous aerial machines. We use stable-baselines to train UAV agent with Deep Q-Networks and Proximal Policy Optimization algorithms.
+
+| Authors: Srivatsan Krishnan, Behzad Boroujerdian, William Fu, Aleksandra Faust, Vijay Janapa Reddi
+| Email: srivatsan@seas.harvard.edu
+| Github: https://github.com/harvard-edge/airlearning
+| Paper: https://arxiv.org/pdf/1906.00421.pdf
+| Video: https://www.youtube.com/watch?v=oakzGnh7Llw (Simulation), https://www.youtube.com/watch?v=cvO5YOzI0mg (on a CrazyFlie Nano-Drone)