Continuous Control with Deep Deterministic Policy Gradient (DDPG)

Introduction

In this project we are going 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.

Distributed Training

For this project, there are 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

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

Dependencies

To set up the python environment to run the code in this repository, the easiest way is to import the project environment in your conda.

Linux, Ubuntu 18.04 LTS:

conda env create -f drlnd_reacher.yml

If the above has not worker or is not a feasible solution, follow the instructions below.

Create (and activate) a new environment with Python 3.6.

Linux or Mac:

conda create --name drlnd python=3.6
source activate drlnd

Windows:

conda create --name drlnd python=3.6 
activate drlnd

Follow the instructions in this repository to perform a minimal install of OpenAI gym.
- Next, install the classic control environment group by following the instructions here.
- Then, install the box2d environment group by following the instructions here.
Clone this repository, and navigate to the python/ folder. Then, install several dependencies.

git clone https://github.com/udacity/deep-reinforcement-learning.git
cd deep-reinforcement-learning/python
pip install .

If you are going to use Jupyter Notebook, create an IPython kernel for the drlnd environment.

python -m ipykernel install --user --name drlnd --display-name "drlnd"

Before running code in a notebook, change the kernel to match the drlnd environment by using the drop-down Kernel menu.

Environment

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
- Version 2: Twenty (20) Agents
  - 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.

(For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link (version 1) or this link (version 2) to obtain the "headless" version of the environment. You will not be able to watch the agent without enabling a virtual screen, but you will be able to train the agent. (To watch the agent, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)
Place the file in the DRLND GitHub repository, in the p2_continuous-control/ folder, and unzip (or decompress) the file.

Instructions

Follow the instructions in Continuous_Control.ipynb to get started with training your own agent!

The code is prepared to run it either with 1 agent or 20 agents, as the mode in which the experience tuples are collected is different in both cases. Furthermore, it has been coded one of the variants of Prioritized Experience Replay (thanks https://github.com/rlcode/per) and one of the parts of the well known TD3 algorithm (which could be seen as an evolved DDPG)

In the DRLND_P2_Report.pdf you will find information about the implementation and the obtained results. Check it out and try it yourself!

aklein1995 / Continuous-Control-with-DDPG