Project 3: Collaboration and Competition

Introduction

For this project, you will work with the Tennis environment.

In this environment, two agents control rackets to bounce a ball over a net. If an agent hits the ball over the net, it receives a reward of +0.1. If an agent lets a ball hit the ground or hits the ball out of bounds, it receives a reward of -0.01. Thus, the goal of each agent is to keep the ball in play.

The observation space consists of 8 variables corresponding to the position and velocity of the ball and racket. Each agent receives its own, local observation. Two continuous actions are available, corresponding to movement toward (or away from) the net, and jumping.

The task is episodic, and in order to solve the environment, your agents must get an average score of +0.5 (over 100 consecutive episodes, after taking the maximum over both 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 2 (potentially different) scores. We then take the maximum of these 2 scores.
• This yields a single score for each episode.

The environment is considered solved, when the average (over 100 episodes) of those scores is at least +0.5.

Getting Started

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

   • 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 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.)

2. Place the file in the DRLND GitHub repository, in the p3_collab-compet/ folder, and unzip (or decompress) the file.

Instructions

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

(Optional) Challenge: Crawler Environment

After you have successfully completed the project, you might like to solve the more difficult Soccer environment.

In this environment, the goal is to train a team of agents to play soccer.

You can read more about this environment in the ML-Agents GitHub here. To solve this harder task, you'll need to download a new Unity environment. (Note: Udacity students should not submit a project with this new environment.)

You need only select the environment that matches your operating system:

• Linux: click here
• Mac OSX: click here
• Windows (32-bit): click here
• Windows (64-bit): click here

Then, place the file in the p3_collab-compet/ folder in the DRLND GitHub repository, and unzip (or decompress) the file. Next, open Soccer.ipynb and follow the instructions to learn how to use the Python API to control the agent.

(For AWS) If you'd like to train the agents on AWS (and have not enabled a virtual screen), then please use this link to obtain the "headless" version of the environment. You will not be able to watch the agents without enabling a virtual screen, but you will be able to train the agents. (To watch the agents, you should follow the instructions to enable a virtual screen, and then download the environment for the Linux operating system above.)

Training

Run train.py file to train the agent

python train.py

Implementation details

1. The algorithm being used is from this paper, Continuous Control with Deep Reinforcement Learning, by researchers at Google Deepmind.
2. We have 2 agents which uses their separate networks to learn.
3. Both the agents share the same replay buffer memory.
4. Batch Normalisation has helped to achieve the results faster.
5. Used the Ornstein-Uhlenbeck process, as suggested by Google DeepMind

Model

We are using Actor Critic Algortihm with these specifications:

Actor:

1. Input Layer size: 24
2. Hidden Layers: [128, 256]
3. Batch Normalization
4. Hidden Layer Activation Function: Relu
5. Output Layer size: 4
6. Output Layer Activation Function: tanh

Critic:

1. Input Layer size: 24
2. First Hidden Layer size: 128
3. Batch Normalization
4. Activation function: Relu
5. Concatenation of Actor output and first hidden layer output
6. Second Hidden Layer size: 256
7. Output size: 1

Output

1. Environment is solved in 4887 episodes to reach an average reward of 0.5 over 100 consecutive episodes.
2. Weights are being saved at checkpoint_actor1.pth, checkpoint_critic1.pth, checkpoint_actor2.pth, checkpoint_critic2.pth
3. The graph below shows the final results:

Testing

Agents can be tested by using the test.py file which loads the learned weights.

python test.py

Ideas for future work:

1. Use collaborative multi agent deep learning algorithms.
2. Add more stability to the agents. In the current work agents are moving a lot and they move to the net everytime.