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Deep Reinforcement Learning Algorithms

Testing Deep RL Algorithms with Lunar Lander and Atari Games.

Pong	Breakout

To Install:

git clone repo
Create virtual environment
pip install -r requirements.txt
(if using testStable.py file, install requirementsStable.txt)

To run:

Run main.py

Options:

All options for testing are changed within settings.json file

To change algorithm change 'rlOption' from 1-5

1 is DQN (set 'ddqn' variable to either true or false for Double DQN).
2 is Expected SARSA
3 is REINFORCE
4 is Vanilla Actor Critic
5 is A2C (note: this one does not perform correctly)

To change game, change 'Game' variable:

1 is Pong
2 is Breakout
3 is Space Invaders

To change Model, use either 'Advanced' or 'Basic':

'Advanced' Model is Convolutional Layer Pytorch network.
'Basic' Model, when this is selected the game will automatically be the Lunar Lander Environment.

Results:

Run plot.py to then visualize test run.
playModel.py can also be used to play trained model weights, this must be selected however in the playModel file.