Project 2: Continuous Control

NOTE: I trained it on Udacity workspace and uploaded the model and notebook here.

Introduction

For this project, I worked on Reacher environment (Version 1)

Reward

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.

State Space

The observation space consists of 33 variables corresponding to position, rotation, velocity, and angular velocities of the arm.

Action Space

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.

Goal

The task is episodic, and in order to solve the environment, your agent must get an average score of +30 over 100 consecutive episodes.

Getting Started

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

   • Version 1: One (1) Agent
     • 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.

Train Agent

Execute Continuous_Control.ipynb to train your own agent! It is based on DDPG(Paper).

The entire notebook can be executed by pressing play icon

The trained agents would automatically get saved in models/ folder for each of the algorithms

Folder Structure

• agents contains the code for all the types of agents
• buffers contains the code for replay buffer which all the algorithms use
• models contains the saved models generates by the code
• networks contains the code for neural networks being used by all the algorithms
• resources contains all the resources related with project