rohbot / drl-continuous-control

Continuous Control Project for Deep Reinforcement Learning Nanodegree

Project 2: Continuous Control

Introduction

For this project, a Deep Reinforcement Learning agent will be trained to work with the Reacher environment.

In this environment, a double-jointed arm can move to target locations. A reward of +0.1 is provided for each step that the agent's hand is in the goal location. Thus, the goal of your agent is to maintain its position at the target location for as many time steps as possible.

The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm. Each action is a vector with four numbers, corresponding to torque applicable to two joints. Every entry in the action vector should be a number between -1 and 1.

Getting Started

1. Clone repository

git clone https://github.com/rohbot/drl-continuous-control.git

2. Install Dependencies

pip install -r requirements.txt

3. Download the environment from one of the links below.
   • Linux: click here
   • Mac OSX: click here
   • Windows (32-bit): click here
   • Windows (64-bit): click here

Training Agent

To start training, run train.py in terminal window:

python train.py

Running Trained Agent

To run trained agent, run play.py in terminal window:

python play.py

Trained model weights are included to see the agent solved the Unity ML Reacher Environment.