Teach AI to Play Arcade Games in the Atari Gym
It is a method that uses two neural networks to optimize performance. The Actor Network is the AI agent that plays the game while the Critic Network classifies the game as a good or a bad one.
For more info go here.
Download the entire repositry, pre-trained models are included for SpaceInvaders, Breakout and Pong.
To check list of all possible arguments run
ML~ python game.py --help
usage: game.py [-h] [--env ENV] [--processes PROCESSES] [--render RENDER]
[--test TEST] [--rnn_steps RNN_STEPS] [--lr LR] [--seed SEED]
[--gamma GAMMA] [--tau TAU] [--horizon HORIZON]
[--hidden HIDDEN]
optional arguments:
-h, --help show this help message and exit
--env ENV gym environment default:SpaceInvaders-v0
--processes PROCESSES
number of processes to train with
--render RENDER renders the atari environment
--test TEST sets lr=0, chooses most likely actions
--rnn_steps RNN_STEPS
steps to train LSTM over
--lr LR learning rate
--seed SEED seed random # generators (for reproducibility)
--gamma GAMMA rewards discount factor
--tau TAU generalized advantage estimation discount
--horizon HORIZON horizon for running averages
--hidden HIDDEN hidden size of GRU
For eg.
python game.py --env Breakout-v0
Note: The list of possible games can be found here.
For doubts email me at: [email protected]