Reinforcement Learning for solving the custom cartpole balance problem in gazebo environment using Proximal Policy Optimization(PPO).

• Custom cartpole environment(from OpenAI gym) in gazebo.
• Observation Space: 4 (continuos)
• Action Space: 2 (discrete)
• Reward range:

• Ubuntu 16.04 (http:https://releases.ubuntu.com/16.04/)
• ROS Kinetic (http:https://wiki.ros.org/kinetic)
• Gazebo 7 (http:https://gazebosim.org/)
• TensorFlow: 1.1.0 (https://www.tensorflow.org/) [with GPU support]
• gym: 0.9.3 (https://github.com/openai/gym)
• Python 3.6

• cartpole_gazebo contains the robot model(both .stl files & .urdf file) and also the gazebo launch file.

• cartpole_controller contains the reinforcement learning implementation of Proximal Policy Optimization(PPO) for custom cartpole environment.

python3 ppo_train.py

python3 ppo_test.py

• Proximal Policy Optimization by OpenAI: https://blog.openai.com/openai-baselines-ppo/
• PPO implementation using TensorFlow: https://github.com/uidilr/ppo_tf

• Use Tensorboard for plotting the training and testing graphs.

Arun Kumar (https://github.com/ioarun)