abheesht17 / reacher-udacity-deep-reinforcement-learning

Solving the famous Reacher Environment using DDPG

ReacherEnvironmentDL

Using DDPG , training 20 agents to play in the Reacher Unity Environment

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This project works with the Reacher environment.

Project Details

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

It is recommended to follow the Udacity DRL ND dependencies instructions here

This project utilises Unity ML-Agents, NumPy and PyTorch

A prebuilt simulator is required in be installed. You need only select the environment that matches your operating system:

Version 1: One (1) Agent

Linux: click here Mac OSX: click here Windows (64-bit): click here

Version 2: Twenty (20) Agents (Used in this project)

Linux: click here Mac OSX: click here Windows (64-bit): click here

The file needs to placed in the root directory of the repository and unzipped.

Next, before starting the environment utilising the corresponding prebuilt app from Udacity Before running the code cell in the notebook, change the file_name parameter to match the location of the Unity environment that you downloaded.

Instructions

Then run the DDPG_Continuous_Control.ipynb notebook using the drlnd kernel to train the DDPG agent.

Once trained the model weights will be saved in the same directory in the files checkpointactor.pth and checkpointcritic.pth.