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Solving the DRLND Unity Navigation Environment

Train an agent to solve the Banana Unity Environment from the Deep Reinforcement Learning Nanodegree on Udacity.

Trained Agent +12 Score Reward Plot Training 2000 Episodes

After 600 episodes of training, the agent achieved an average score of +13 over 100 consecutive episodes. The trained agent playing in the left GIF above achieved a score of +12 in that instance. The environment is considered solved if the average reward over 100 consecutive episodes is above +13.

The task was solved using a simple DQN architecture with experience replay and using using a separate network that stays fixed for C updates to generate the targets on the 37 dimensional state vector. The DQN learns a parametric approximation of the action-value function that maps the state vector to actions, by minimizing the mean-squared error. More information found in the detailed report.

Training

Trained Agent +12 Score

Prerequisites

  • conda or miniconda (recommended)
  • make
  • Download the environment that matches your OS following the Getting Started from the DRLND repo and unpack it in the root of this project

Install Environment

Automated Install

Simply run make install to install all requirements in a conda environment called drlnd_nav.

Manual Install

Create a conda environment called drlnd_nav with Python3.6 and activate it using the following commands

conda create --name drlnd_nav python=3.6
conda activate drlnd_nav

Then install the requirements file requirements.txt and install the drlnd_nav ipykernel.

pip install -r $(PWD)/requirements.txt
python -m ipykernel install --user --name drlnd_nav --display-name "drlnd_nav"

Run the Code

Next, run make start to start the Jupyter notebook server and use your favorite browser to navigate to http://localhost:8888/?token=abcd.

Train an Agent

To train an agent start the kW_Solution-Train.ipynb notebook and run all the cells (this will also plot the rewards over the number of episodes and save the model weights to disk).

Watch a Trained Agent

To watch a successful agent solve the environment, run all the cells in kW_Solution-Watch.ipynb.