danielpalaio/pong-v4_deeprl

Atari OpenAI Pong-v4 DeepRL-based solutions (DQN, DuelingDQN, D3QN)

OpenAI Pong-v4 DeepRL-based solutions

Investigation under the development of the master thesis "DeepRL-based Motion Planning for Indoor Mobile Robot Navigation" @ Institute of Systems and Robotics - University of Coimbra (ISR-UC)

Software/Requirements

Module	Software/Hardware
Python IDE	Pycharm
Deep Learning library	Tensorflow + Keras
GPU	GeForce GeForce GTX 1060
Interpreter	Python 3.8
Packages	requirements.txt

To setup Pycharm + Anaconda + GPU, consult the setup file here.
To import the required packages (requirements.txt), download the file into the project folder and type the following instruction in the project environment terminal:

pip install -r requirements.txt

⚠️ WARNING ⚠️

The training process generates a .txt file that track the network models (in 'tf' and .h5 formats) which achieved the solved requirement of the environment. Additionally, an overview image (graph) of the training procedure is created.
To perform several training procedures, the .txt, .png, and directory names must be change. Otherwise, the information of previous training models will get overwritten, and therefore lost.

Regarding testing the saved network models, if using the .h5 model, a 5 episode training is required to initialize/build the keras.model network. Thus, the warnings above mentioned are also appliable to this situation.
Loading the saved model in 'tf' is the recommended option. After finishing the testing, an overview image (graph) of the training procedure is also generated.

OpenAI Atari Pong-v4

Actions:
0 - No action
1 - No action
2 - Racket go up
3 - Racket go down
4 - Racket go up
5 - Racket go down
Actions (2 & 4) and (3 & 5) - Same movement with different amplitudes

States:
Stack of 4 (80, 80) cropped grey-scaled images (6400 pixels)

Rewards:
Scalar value (1) for a winning rally
Scalar value (-1) for a losing rally

Episode termination:
Player reaches a score of 21
Episode length > 400000

Solved Requirement:
Average score of 17 over 100 consecutive trials

Deep Q-Network (DQN)

Train	Test
Parameter Value Number of episodes 400 Learning rate 0.0001 Discount Factor 0.99 Epsilon 1.0 Batch size 32 TargetNet update rate (steps) 1000 Actions 6 States (4, 80, 80)	Parameter Value Number of episodes 100 Epsilon 0.01 Actions 6 States (4, 80, 80)

Network model used for testing: 'saved_networks/dqn_model10' ('tf' model, also available in .h5)

Dueling DQN

Train	Test
Parameter Value Number of episodes 300 Learning rate 0.0001 Discount Factor 0.99 Epsilon 1.0 Batch size 32 TargetNet update rate (steps) 1000 Actions 6 States (4, 80, 80)	Parameter Value Number of episodes 100 Epsilon 0.01 Actions 6 States (4, 80, 80)

Network model used for testing: 'saved_networks/duelingdqn_model30' ('tf' model, also available in .h5)

Dueling Double DQN (D3QN)

Train	Test
Parameter Value Number of episodes 400 Learning rate 0.0001 Discount Factor 0.99 Epsilon 1.0 Batch size 32 TargetNet update rate (steps) 1000 Actions 6 States (4, 80, 80)	Parameter Value Number of episodes 100 Epsilon 0.01 Actions 6 States (4, 80, 80)

Network model used for testing: 'saved_networks/d3qn_model50' ('tf' model, also available in .h5)