erichans / udacity-deeprl-navigation

Navigation problem using Reinforcement Learning

udacity-deeprl-navigation

Navigation problem using Deep Reinforcement Learning Agent

Project Details

Unity ML-Agents

Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents.

For game developers, these trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release.

In this course, you will use Unity's rich environments to design, train, and evaluate your own deep reinforcement learning algorithms. You can read more about ML-Agents by perusing the GitHub repository.

The Environment

For 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 the 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.

Getting Started

Dependencies

To set up your python environment to run the code in this repository, follow the instructions below.

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

   • Windows:

   conda create --name drlnd python=3.6 
   activate drlnd

   • Linux or Mac:

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

2. Install pytorch >= 1.4.0

3. Follow the instructions in this repository to perform a minimal install of OpenAI gym.

   • Next, install the classic control environment group by following the instructions here.
   • Then, install the box2d environment group by following the instructions here.

4. Clone the repository (if you haven't already!), and navigate to the python/ folder. Then, install several dependencies.

git clone https://github.com/erichans/udacity-deeprl-navigation.git
cd udacity-deeprl-navigation/python
pip install .

Instructions

Train the Agent

python train.py

You can tune the model by changing the following hyperparameters in following files (default values below):

train.py

• TOTAL_EPISODES = 500
• EPSILON_START = 1.0
• EPSILON_DECAY = .99
• EPSILON_END = 0.01
• DOUBLE_DQN = True
  • See paper here
• DUELING_DQN = True
  • See paper here

agent.py

• LR = 5e-4 (learning rate)

• BUFFER_SIZE = 100.000

• BATCH_SIZE = 64

• GAMMA = .99 (discount factor)

• UPDATE_EVERY = 4 (How many steps to wait before update the target QNetwork)

• TAU = 1e-3 (soft update from local QNetwork parameters to target QNetwork parameters)

Report

To see more details like:

• Learning Algorithm
• Plot of Rewards
• Ideas for Future Work

Check the Report