Navigation_RL_Agent

Training an RL agent to solve unity's Banana navigation environment.

Project Details:

Overview: Broad goal is to train an agent to successfully navigate Unity's large square world environment of yellow and blue bananas.

Goal & Rewards: A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana. Thus, the goal of the agent is to collect as many yellow bananas as possible while avoiding blue bananas.

State Space: The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around the agent's forward direction.

Action Space: Four discrete actions are available, corresponding to:

• 0 - move forward.
• 1 - move backward.
• 2 - turn left.
• 3 - turn right.

Solving condition: The task is episodic, and in order to solve the environment, the agent must get an average score of +13 over 100 consecutive episodes.

Getting Started:

Setting up the python environment and installing dependencies

1. Create (and activate) a new environment with Python 3.6.

• Linux or Mac:

  conda create --name navigation python=3.6 source activate navigation

• Windows:

  conda create --name navigation python=3.6

  activate navigation

2. Clone the repository, Then, install several dependencies.

   git clone https://github.com/ramanshrivastava/navigantion_rl.git

   pip install torch

   pip install numpy

   pip install matplotlib

3. Create an IPython kernel for the navigation environment.

   python -m ipykernel install --user --name navigation --display-name "navigation"

4. Before running code in a notebook, change the kernel to match the navigation environment by using the drop-down Kernel menu.

Download the Unity Environment

1. Download the environment from one of the links below. You need only select the environment that matches your operating system:

• Linux: click here
• Mac OSX: click here
• Windows (32-bit): click here
• Windows (64-bit): click here

(For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.

(For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the environment.

2. Place the file in the navigation_rl GitHub repository, in the root folder, and unzip (or decompress) the file.

Instructions:

1. Open the Navigation.ipynb jupyter notebook and execute each code cell to train the agent.
2. Once the necessary packages are imported as a next step provode the file_name for the UnityEnvironment
3. Execute the rest of the cells as it is there after
4. provide the PATH for .pt file in the code cell 7 and 8 to save and load model weights.