/P2-Continous-Control-DeepRL

Group of double-jointed Robotic Arm were trained to follow a target location. The goal of the agents were to maintain its position at the target location.

Primary LanguageJupyter Notebook

Train a double-jointed Robotic Arm

Udacity Deep Reinforcement Learning Nanodegree

Project 2 - Continous Control

Introduction

The project is based on Unity Environment. The Agent is trained to follow a target location!

Before Training After Training

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.

For this project, we will provide you with two separate versions of the Unity environment:

  • The first version contains a single agent.
  • The second version contains 20 identical agents, each with its own copy of the environment.

The second version is useful for algorithms like PPO, A3C, and D4PG that use multiple (non-interacting, parallel) copies of the same agent to distribute the task of gathering experience.

Solving the environment

Note that your project submission need only solve one of the two versions of the environment.

Option 1: Solve the First Version

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

Option 2: Solve the Second Version

The barrier for solving the second version of the environment is slightly different, to take into account the presence of many agents. In particular, your agents must get an average score of +30 (over 100 consecutive episodes, and over all agents). Specifically,

After each episode, we add up the rewards that each agent received (without discounting), to get a score for each agent. This yields 20 (potentially different) scores. We then take the average of these 20 scores. This yields an average score for each episode (where the average is over all 20 agents).

The environment is considered solved, when the average (over 100 episodes) of those average scores is at least +30.

Getting Started

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

    (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.

  3. Install conda environment with conda env create -f environment.yml

Instruction

To train the agent, start jupyter notebook, open Continous_Control.ipynb and execute! For more information, please check instructions inside the notebook.

Result

Plot showing the score of the model during training.