Project 1: Navigation

Links:

• Navigation.ipynb
• Report.pdf
• Saved model weights

Introduction

In this project, you will train an agent to navigate (and collect bananas!) in a large, square world.

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 your agent is to collect as many yellow bananas as possible while avoiding blue bananas.

The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around agent's forward direction. Given this information, the agent has to learn how to best select actions. Four discrete actions are available, corresponding to:

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

The task is episodic, and in order to solve the environment, your agent must get an average score of +13 over 100 consecutive episodes. This means the agent must collect on average at least 13 yellow bananas without running into a blue one.

Example of a trained Model

(I trained this one longer than required by project requirements)

Getting Started

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 DRLND GitHub repository, in the p1_navigation/ folder, and unzip (or decompress) the file.

Installation Instructions

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

   • Linux or Mac:

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

   • Windows:

   conda create --name drlnd python=3.6 
   activate drlnd

2. Install the dependencies:

   pip install .
   pip install pandas

   If you get the error message that torch=0.4.0 could not be found, try the following

   conda install pytorch=0.4.0 -c pytorch

3. Run the Navigation.py file or start a jupyter server and run Navigation.ipynd

Instructions

As mentioned in Point 3 in the Installation Instructions you can either run the jupyter notebook Navigation.ipynd, or the python script Navigation.py. After initializing the UnityEnvironment, a Agent object instance is created. All hyperparameters attributable to the Agent can be set over the constructor.

agent = Agent(state_size, action_size, hidden_sizes=[70, 64],
              gamma=0.992, lr=0.0005, tau=0.002,
              buffer_size=100000, batch_size=64, update_rate=10,
              seed=0)

After creating the agent instance you can either train the agent by passing it to the train_agent()function, or you can watch the agent interacting with its environment by calling the watch_agent() function. If you want to load the weights for the local network you can also call the watch_agent_from_pth_file() function.

# with this boolean you can decide if you just want to watch an agent or train the agent yourself
watch_only = True
if watch_only:
    watch_agent_from_pth_file(_env, _brain_name, _agent, './checkpoint.pth')
else:
    scores = train_agent(_env, _brain_name, _agent, n_episodes=1000,
                         eps_start=1, eps_decay=0.995, eps_cutoff=420, eps_end=0.01)
    watch_agent(_env, _brain_name, _agent)
    plot_scores(scores=scores)